Datasets:

james-burton

/

aug-text-exps-v3

like 0

LogisticRegression

C3

Cab Surge Pricing System

The predicted label is C3 given the predictability of C1 is 28.96% and that of C2 is 23.41%. Considering the probabilities of the classes, the model can be described as being moderately confident. The prediction of C3 can be attributed to the varying degree of contributions of the input features. Attribution analysis indicates that F1, F8, and F2 are considered the most influential. Those with moderate influence are F4, F6, F10, F3, F5, and F9, whereas on the contrary, the least influential ones are F11, F7, and F12. The analysis also revealed that not all the features contribute positively to the prediction decision and amongst the input features, the ones with negative attributions decreasing the likelihood of the C3 prediction are F8, F2, F10, F3, and F5 whereas conversely, the top positive features are F1, F4, and F6.

{'C1': '28.96%', 'C2': '23.41%', 'C3': '47.63%'}

{'F1': 'Type_of_Cab', 'F8': 'Confidence_Life_Style_Index', 'F2': 'Destination_Type', 'F4': 'Trip_Distance', 'F6': 'Cancellation_Last_1Month', 'F10': 'Life_Style_Index', 'F3': 'Customer_Rating', 'F5': 'Var3', 'F9': 'Var1', 'F11': 'Customer_Since_Months', 'F7': 'Var2', 'F12': 'Gender'}

{'F2': 'F1', 'F5': 'F8', 'F6': 'F2', 'F1': 'F4', 'F8': 'F6', 'F4': 'F10', 'F7': 'F3', 'F11': 'F5', 'F9': 'F9', 'F3': 'F11', 'F10': 'F7', 'F12': 'F12'}

{'C3': 'C1', 'C1': 'C2', 'C2': 'C3'}

C3

{'C1': 'Low', 'C2': 'Medium', 'C3': 'High'}

RandomForestClassifier

C2

Company Bankruptcy Prediction

The model outputs a predicted probability of 2.55% for the C1 label and 97.45% for the C2 label. Judging from above, the most probable class is C2. Hence, C2 is the assigned label by the model, with a very high confidence level. The top features contributing to the prediction assessment above are F24, F41, F62, F38, and F7. However, the values of about twenty features are deemed relevant while the remaining are regarded as irrelevant when classifying the given case. These irrelevant features include F4, F19, F43, and F45. Among the relevant features, F62, F47, F23, F53, F33, and F58 are shown to be the only positive features that increase the model's response in favour of the assigned label C2. In contrast, the majority of the relevant features, mainly F24, F41, F38, and F7, have negative contributions, decreasing the odds of the label C2, hence supporting the assignment of C1 to the given case.

{'C1': '2.55%', 'C2': '97.45%'}

{'F24': " Net Income to Stockholder's Equity", 'F41': ' Total income\\/Total expense', 'F62': ' Borrowing dependency', 'F38': ' Continuous interest rate (after tax)', 'F7': ' Net Value Per Share (B)', 'F40': ' Cash\\/Current Liability', 'F56': ' Net worth\\/Assets', 'F47': ' Fixed Assets Turnover Frequency', 'F23': ' Interest-bearing debt interest rate', 'F53': ' No-credit Interval', 'F82': ' Net Value Per Share (A)', 'F36': ' Long-term fund suitability ratio (A)', 'F33': ' Equity to Long-term Liability', 'F9': ' Realized Sales Gross Margin', 'F58': ' Current Asset Turnover Rate', 'F86': ' Working Capital to Total Assets', 'F80': ' Interest Coverage Ratio (Interest expense to EBIT)', 'F68': ' Working capitcal Turnover Rate', 'F2': ' Inventory Turnover Rate (times)', 'F75': ' After-tax net Interest Rate', 'F4': ' Working Capital\\/Equity', 'F19': ' Liability to Equity', 'F43': ' Operating Gross Margin', 'F45': ' Cash Flow Per Share', 'F92': ' Contingent liabilities\\/Net worth', 'F10': ' Operating Profit Per Share (Yuan ¥)', 'F66': ' Operating Profit Rate', 'F46': ' Net Worth Turnover Rate (times)', 'F64': ' Continuous Net Profit Growth Rate', 'F51': ' Long-term Liability to Current Assets', 'F67': ' Fixed Assets to Assets', 'F54': ' Inventory and accounts receivable\\/Net value', 'F65': ' Regular Net Profit Growth Rate', 'F16': ' Current Liability to Equity', 'F59': ' Equity to Liability', 'F49': ' Current Liability to Liability', 'F25': ' Operating profit\\/Paid-in capital', 'F27': ' Net Value Per Share (C)', 'F71': ' Operating Funds to Liability', 'F91': ' Current Liability to Current Assets', 'F89': ' Current Ratio', 'F1': ' Quick Assets\\/Current Liability', 'F85': ' Tax rate (A)', 'F11': ' After-tax Net Profit Growth Rate', 'F39': ' Per Share Net profit before tax (Yuan ¥)', 'F30': ' Total Asset Turnover', 'F73': ' CFO to Assets', 'F76': ' Cash Reinvestment %', 'F78': ' Net profit before tax\\/Paid-in capital', 'F28': ' Cash Flow to Equity', 'F81': ' Debt ratio %', 'F70': ' Current Liabilities\\/Liability', 'F26': ' Interest Expense Ratio', 'F72': ' Cash Flow to Sales', 'F88': ' Total Asset Growth Rate', 'F32': ' Inventory\\/Current Liability', 'F84': ' Allocation rate per person', 'F74': ' Operating Expense Rate', 'F14': ' Operating profit per person', 'F90': ' Net Income to Total Assets', 'F21': ' Net Value Growth Rate', 'F63': ' ROA(B) before interest and depreciation after tax', 'F34': ' Cash Flow to Liability', 'F3': ' Inventory\\/Working Capital', 'F6': ' Retained Earnings to Total Assets', 'F50': ' Total assets to GNP price', 'F44': ' Persistent EPS in the Last Four Seasons', 'F5': ' Total debt\\/Total net worth', 'F13': ' Quick Ratio', 'F61': ' Revenue per person', 'F8': ' Non-industry income and expenditure\\/revenue', 'F15': ' Cash\\/Total Assets', 'F57': ' ROA(A) before interest and % after tax', 'F17': ' ROA(C) before interest and depreciation before interest', 'F18': ' Research and development expense rate', 'F29': ' Cash Flow to Total Assets', 'F22': ' Pre-tax net Interest Rate', 'F31': ' Accounts Receivable Turnover', 'F12': ' Current Liability to Assets', 'F93': ' Quick Assets\\/Total Assets', 'F79': ' Total expense\\/Assets', 'F55': ' Operating Profit Growth Rate', 'F20': ' Average Collection Days', 'F48': ' Current Assets\\/Total Assets', 'F35': ' Current Liabilities\\/Equity', 'F42': ' Realized Sales Gross Profit Growth Rate', 'F60': ' Cash flow rate', 'F69': ' Total Asset Return Growth Rate Ratio', 'F87': ' Degree of Financial Leverage (DFL)', 'F83': ' Cash Turnover Rate', 'F77': ' Quick Asset Turnover Rate', 'F52': ' Revenue Per Share (Yuan ¥)', 'F37': ' Gross Profit to Sales'}

{'F59': 'F24', 'F57': 'F41', 'F3': 'F62', 'F12': 'F38', 'F27': 'F7', 'F32': 'F40', 'F84': 'F56', 'F22': 'F47', 'F1': 'F23', 'F56': 'F53', 'F42': 'F82', 'F52': 'F36', 'F23': 'F33', 'F83': 'F9', 'F61': 'F58', 'F67': 'F86', 'F60': 'F80', 'F73': 'F68', 'F18': 'F2', 'F79': 'F75', 'F68': 'F4', 'F66': 'F19', 'F62': 'F43', 'F65': 'F45', 'F64': 'F92', 'F63': 'F10', 'F58': 'F66', 'F55': 'F46', 'F54': 'F64', 'F69': 'F51', 'F74': 'F67', 'F70': 'F54', 'F85': 'F65', 'F92': 'F16', 'F91': 'F59', 'F90': 'F49', 'F89': 'F25', 'F88': 'F27', 'F87': 'F71', 'F86': 'F91', 'F82': 'F89', 'F71': 'F1', 'F81': 'F85', 'F80': 'F11', 'F78': 'F39', 'F77': 'F30', 'F76': 'F73', 'F75': 'F76', 'F72': 'F78', 'F53': 'F28', 'F47': 'F81', 'F51': 'F70', 'F14': 'F26', 'F25': 'F72', 'F24': 'F88', 'F21': 'F32', 'F20': 'F84', 'F19': 'F74', 'F17': 'F14', 'F16': 'F90', 'F15': 'F21', 'F13': 'F63', 'F50': 'F34', 'F11': 'F3', 'F10': 'F6', 'F9': 'F50', 'F8': 'F44', 'F7': 'F5', 'F6': 'F13', 'F5': 'F61', 'F4': 'F8', 'F26': 'F15', 'F28': 'F57', 'F29': 'F17', 'F30': 'F18', 'F49': 'F29', 'F48': 'F22', 'F2': 'F31', 'F46': 'F12', 'F45': 'F93', 'F44': 'F79', 'F43': 'F55', 'F41': 'F20', 'F40': 'F48', 'F39': 'F35', 'F38': 'F42', 'F37': 'F60', 'F36': 'F69', 'F35': 'F87', 'F34': 'F83', 'F33': 'F77', 'F31': 'F52', 'F93': 'F37'}

{'C1': 'C1', 'C2': 'C2'}

Yes

{'C1': 'No', 'C2': 'Yes'}

SVC

C1

Broadband Sevice Signup

The algorithm identifies the provided data or case as C1 with a greater level of certainty since the prediction probability of class C2 is just 0.07 percent as a result, C2 is less likely than C1. The influence of input features such as F8, F10, F42, F22, and F5 is mostly responsible for the classification verdict above with only F5 having a negative influence among them, slightly pulling the decision in favour of C2. F8, F10, F42, and F22, on the other hand, make considerable positive contributions in favour of assigning C1 to the data. F20, F9, F32, F23, F31, F16, F39, and F35 are some more features that have a modest effect on the algorithm's decision. But, not all features are demonstrated to influence the classification decision either negatively or positively to the aforementioned classification outcome and in reality, a number of these are demonstrated to be irrelevant for determining the suitable label for this case and these include F40, F17, F13, and F36. All in all, the most important features for this classification instance are F8 and F10, whereas F4 and F26 are the least important.

{'C1': '99.93%', 'C2': '0.07%'}

{'F8': 'X38', 'F10': 'X32', 'F42': 'X31', 'F22': 'X25', 'F5': 'X8', 'F20': 'X35', 'F9': 'X1', 'F39': 'X3', 'F23': 'X28', 'F35': 'X19', 'F31': 'X9', 'F32': 'X11', 'F16': 'X10', 'F28': 'X21', 'F11': 'X17', 'F29': 'X4', 'F33': 'X36', 'F27': 'X2', 'F21': 'X6', 'F18': 'X34', 'F17': 'X37', 'F40': 'X40', 'F13': 'X42', 'F36': 'X41', 'F34': 'X5', 'F19': 'X33', 'F30': 'X39', 'F12': 'X24', 'F38': 'X30', 'F1': 'X27', 'F25': 'X26', 'F15': 'X23', 'F3': 'X22', 'F7': 'X20', 'F24': 'X18', 'F6': 'X16', 'F14': 'X15', 'F37': 'X14', 'F26': 'X13', 'F4': 'X12', 'F2': 'X7', 'F41': 'X29'}

{'F35': 'F8', 'F29': 'F10', 'F28': 'F42', 'F23': 'F22', 'F6': 'F5', 'F32': 'F20', 'F40': 'F9', 'F2': 'F39', 'F26': 'F23', 'F17': 'F35', 'F7': 'F31', 'F9': 'F32', 'F8': 'F16', 'F19': 'F28', 'F15': 'F11', 'F3': 'F29', 'F33': 'F33', 'F1': 'F27', 'F4': 'F21', 'F31': 'F18', 'F34': 'F17', 'F37': 'F40', 'F38': 'F13', 'F39': 'F36', 'F41': 'F34', 'F30': 'F19', 'F36': 'F30', 'F22': 'F12', 'F27': 'F38', 'F25': 'F1', 'F24': 'F25', 'F21': 'F15', 'F20': 'F3', 'F18': 'F7', 'F16': 'F24', 'F14': 'F6', 'F13': 'F14', 'F12': 'F37', 'F11': 'F26', 'F10': 'F4', 'F5': 'F2', 'F42': 'F41'}

{'C1': 'C1', 'C2': 'C2'}

No

{'C1': 'No', 'C2': 'Yes'}

SVM

C1

Customer Churn Modelling

For the given dataset instance, the label assigned by the classifier is C1 since it has a predicted probability of about 89.16%. On the other hand, there is a 9.0% chance that C2 could be the appropriate label, whereas C3 only has a 1.84% chance of being the true label. The classifier arrived at this classification verdict chiefly due to the influence and contributions of variables such as F7, F2, F3, and F6. However, there is less emphasis on the values of F9, F4, and F8, since their impact on the classifier with respect to the given case is smaller compared to the other variables, hence they are the least ranked features. From the attribution analysis, there are four variables with negative contributions, pushing the verdict in the direction of C2. These negative variables are F7, F6, F5, and F10, and their influence on the classifier could explain why there is a little bit of doubt about the correctness of the C1 class assigned and the notable positive variables are F2, F1, F9, and F3.

{'C1': '89.16%', 'C2': '9.0%', 'C3': '1.84%'}

{'F7': 'IsActiveMember', 'F2': 'Age', 'F3': 'Geography', 'F6': 'NumOfProducts', 'F5': 'Gender', 'F1': 'Tenure', 'F10': 'CreditScore', 'F9': 'Balance', 'F4': 'EstimatedSalary', 'F8': 'HasCrCard'}

{'F9': 'F7', 'F4': 'F2', 'F2': 'F3', 'F7': 'F6', 'F3': 'F5', 'F5': 'F1', 'F1': 'F10', 'F6': 'F9', 'F10': 'F4', 'F8': 'F8'}

{'C1': 'C1', 'C2': 'C2', 'C3': 'C3'}

Stay

{'C1': 'Stay', 'C2': 'Leave', 'C3': 'Other'}

GradientBoostingClassifier

C2

Basketball Players Career Length Prediction

The case is labelled as C2 by the model but looking at the predicted probabilities across the different classes, there is a 33.63% chance that the label could be C1. To explain the above prediction conclusion, the analysis revealed that the majority of the features have negative influences or attributions, pushing the prediction away from C2 in favour of C1. The negative features include F16, F18, F11, F5, and F1 and the values of these features are ranked higher than any of the positive features. Shifting the prediction in the direction of C2 are the positive features F8, F7, F6, and F19. The analysis also revealed that the values of F10, F15, and F9 are less relevant to the prediction for the case under consideration.

{'C1': '33.63%', 'C2': '66.37%'}

{'F16': 'GamesPlayed', 'F18': 'OffensiveRebounds', 'F11': 'FieldGoalPercent', 'F5': 'FreeThrowPercent', 'F1': '3PointPercent', 'F8': '3PointAttempt', 'F4': 'FieldGoalsMade', 'F13': 'Blocks', 'F14': 'DefensiveRebounds', 'F12': 'Turnovers', 'F7': 'Rebounds', 'F17': 'FreeThrowAttempt', 'F6': 'MinutesPlayed', 'F3': 'Assists', 'F2': 'FieldGoalsAttempt', 'F19': '3PointMade', 'F10': 'PointsPerGame', 'F15': 'FreeThrowMade', 'F9': 'Steals'}

{'F1': 'F16', 'F13': 'F18', 'F6': 'F11', 'F12': 'F5', 'F9': 'F1', 'F8': 'F8', 'F4': 'F4', 'F18': 'F13', 'F14': 'F14', 'F19': 'F12', 'F15': 'F7', 'F11': 'F17', 'F2': 'F6', 'F16': 'F3', 'F5': 'F2', 'F7': 'F19', 'F3': 'F10', 'F10': 'F15', 'F17': 'F9'}

{'C1': 'C1', 'C2': 'C2'}

Less than 5

{'C1': 'More than 5', 'C2': 'Less than 5'}

BernoulliNB

C1

Customer Churn Modelling

The most likely label chosen by the model in this case is C1. The decision above is based on the prediction probabilities for the two possible labels, C1 and C2, which are 94.25% and 5.75%, respectively. The following variables can be ranked from most important to least important based on their contribution to the model when it comes to this instance: F7, F10, F3, F2, F6, F9, F1, F8, F4, and F5. F10 and F7 turned out to be the most important positive variables, supporting the model towards assigning the class C1. The least positive variables are F8 and F1, which have less effect on the model. In fact, most of the input features have negative contributions towards the assignment of class C1, leading to a decision change in favour of the other label, C2. The most negative variables are F6, F3, and F2, and the least negative are F4 and F5.

{'C1': '94.25%', 'C2': '5.75%'}

{'F7': 'IsActiveMember', 'F10': 'NumOfProducts', 'F3': 'Gender', 'F2': 'Geography', 'F6': 'Age', 'F9': 'CreditScore', 'F1': 'EstimatedSalary', 'F8': 'Balance', 'F4': 'HasCrCard', 'F5': 'Tenure'}

{'F9': 'F7', 'F7': 'F10', 'F3': 'F3', 'F2': 'F2', 'F4': 'F6', 'F1': 'F9', 'F10': 'F1', 'F6': 'F8', 'F8': 'F4', 'F5': 'F5'}

{'C1': 'C1', 'C2': 'C2'}

Stay

{'C1': 'Stay', 'C2': 'Leave'}

BernoulliNB

C1

Personal Loan Modelling

As per the classification algorithm employed, the most probable label for the data under consideration is C1 since the chances of C2 is very slim and negligible. The main driver behind the labelling decision above is F2. The features with moderate influence are F5, F8, F4, F7, F9, and F3, while those with very small or marginal impact are F6 and F1. The direction of influence of the input features could be used to explain why the algorithm is very confident here. Most of the features have a positive impact, increasing or improving the chances of C1 being the correct label and the feature with a significantly higher contribution, F2, is a positive feature which when coupled with other positives F8, F4, F3, and F9 encourages the prediction or assignment of the C1 label. Furthermore, aside from F5 and F7, the other two negative features, F6 and F1, are shown to have a significantly lower impact on the algorithm and the very marginal doubt in the decision can be attributed to the influence of the negative features.

{'C1': '99.99%', 'C2': '0.01%'}

{'F2': 'CD Account', 'F5': 'Income', 'F8': 'CCAvg', 'F4': 'Securities Account', 'F7': 'Education', 'F9': 'Family', 'F3': 'Mortgage', 'F6': 'Age', 'F1': 'Extra_service'}

{'F8': 'F2', 'F2': 'F5', 'F4': 'F8', 'F7': 'F4', 'F5': 'F7', 'F3': 'F9', 'F6': 'F3', 'F1': 'F6', 'F9': 'F1'}

{'C2': 'C1', 'C1': 'C2'}

Reject

{'C1': 'Reject', 'C2': 'Accept'}

MLPClassifier

C1

Vehicle Insurance Claims

The ML algorithm classifies the provided data or case as C1 with a likelihood of 80.70%, hinting that the likelihood of C2 being the correct label is only 19.30%. This classification decision above is mainly based on the influence or contributions of the input features. The most relevant features driving the classification algorithm to arrive at the above decision are F13, F2, F18, F8, F22, F33, and F17. On the other side, not all of the input features are considered relevant when deciding the appropriate label for the given data instance, and these irrelevant features include F30, F31, F29, F10, and F11. Among the top influential features, F22, F33, and F17 are regarded as negative features since their contributions push the algorithm's decision towards the less likely class, C2, although F13, F2, F18, F25, and F8 have positive contributions, increasing the probability that C1 is the right label here.

{'C2': '19.30%', 'C1': '80.70%'}

{'F13': 'incident_severity', 'F2': 'insured_relationship', 'F8': 'authorities_contacted', 'F18': 'vehicle_claim', 'F33': 'umbrella_limit', 'F17': 'insured_hobbies', 'F22': 'incident_type', 'F25': 'policy_deductable', 'F19': 'auto_make', 'F9': 'number_of_vehicles_involved', 'F20': 'insured_occupation', 'F26': 'property_damage', 'F32': 'incident_state', 'F28': 'auto_year', 'F15': 'capital-loss', 'F16': 'policy_csl', 'F6': 'collision_type', 'F24': 'capital-gains', 'F7': 'property_claim', 'F3': 'incident_hour_of_the_day', 'F30': 'police_report_available', 'F31': 'policy_annual_premium', 'F29': 'incident_city', 'F11': 'insured_zip', 'F10': 'bodily_injuries', 'F1': 'injury_claim', 'F4': 'witnesses', 'F23': 'total_claim_amount', 'F12': 'insured_education_level', 'F14': 'insured_sex', 'F27': 'policy_state', 'F5': 'age', 'F21': 'months_as_customer'}

{'F27': 'F13', 'F24': 'F2', 'F28': 'F8', 'F16': 'F18', 'F5': 'F33', 'F23': 'F17', 'F25': 'F22', 'F3': 'F25', 'F33': 'F19', 'F10': 'F9', 'F22': 'F20', 'F31': 'F26', 'F29': 'F32', 'F17': 'F28', 'F8': 'F15', 'F19': 'F16', 'F26': 'F6', 'F7': 'F24', 'F15': 'F7', 'F9': 'F3', 'F32': 'F30', 'F4': 'F31', 'F30': 'F29', 'F6': 'F11', 'F11': 'F10', 'F14': 'F1', 'F12': 'F4', 'F13': 'F23', 'F21': 'F12', 'F20': 'F14', 'F18': 'F27', 'F2': 'F5', 'F1': 'F21'}

{'C1': 'C2', 'C2': 'C1'}

Fraud

{'C2': 'Not Fraud', 'C1': 'Fraud'}

SVC

C2

Broadband Sevice Signup

The predicted probability of class C1 is 12.81% and that of class C2 is 87.19%. Therefore, the label chosen by the model is C2, which is the most probable class. The top two features with significant influence on the prediction verdict above are F17 and F7. These features have positive attributions, shifting the decision higher in support of label C2. Other positive features are F39, F25, F3, and F1. Decreasing the likelihood of the assigned label are the negative features such as F31, F34, F18, and F20. Finally, the values of features such as F21, F32, F35, F19, F30, and F8 are considered irrelevant to the prediction decision above.

{'C1': '12.81%', 'C2': '87.19%'}

{'F17': 'X38', 'F7': 'X32', 'F31': 'X22', 'F39': 'X35', 'F25': 'X25', 'F34': 'X16', 'F18': 'X12', 'F20': 'X31', 'F9': 'X3', 'F29': 'X9', 'F40': 'X1', 'F24': 'X19', 'F6': 'X4', 'F3': 'X2', 'F42': 'X29', 'F14': 'X42', 'F1': 'X36', 'F37': 'X21', 'F4': 'X40', 'F26': 'X10', 'F21': 'X33', 'F32': 'X5', 'F35': 'X6', 'F19': 'X41', 'F8': 'X39', 'F30': 'X7', 'F36': 'X37', 'F33': 'X8', 'F28': 'X34', 'F2': 'X18', 'F13': 'X17', 'F12': 'X11', 'F27': 'X30', 'F10': 'X28', 'F11': 'X27', 'F23': 'X26', 'F16': 'X13', 'F38': 'X14', 'F5': 'X23', 'F22': 'X15', 'F41': 'X20', 'F15': 'X24'}

{'F35': 'F17', 'F29': 'F7', 'F20': 'F31', 'F32': 'F39', 'F23': 'F25', 'F14': 'F34', 'F10': 'F18', 'F28': 'F20', 'F2': 'F9', 'F7': 'F29', 'F40': 'F40', 'F17': 'F24', 'F3': 'F6', 'F1': 'F3', 'F42': 'F42', 'F38': 'F14', 'F33': 'F1', 'F19': 'F37', 'F37': 'F4', 'F8': 'F26', 'F30': 'F21', 'F41': 'F32', 'F4': 'F35', 'F39': 'F19', 'F36': 'F8', 'F5': 'F30', 'F34': 'F36', 'F6': 'F33', 'F31': 'F28', 'F16': 'F2', 'F15': 'F13', 'F9': 'F12', 'F27': 'F27', 'F26': 'F10', 'F25': 'F11', 'F24': 'F23', 'F11': 'F16', 'F12': 'F38', 'F21': 'F5', 'F13': 'F22', 'F18': 'F41', 'F22': 'F15'}

{'C2': 'C1', 'C1': 'C2'}

Yes

{'C1': 'No', 'C2': 'Yes'}

MLPClassifier

C2

Ethereum Fraud Detection

The C1 has a predicted probability of just 3.10% while that of the C2 is 96.90%, therefore, the most likely class selected by the classifier for the given data is C2. The relevant features contributing to this classification are mainly F10, F30, F24, F33, F6, F36, F28, F23, F34, F5, F8, F15, F1, F22, F25, F38, F7, F37, F12, and F29. As per the attribution analysis, F10 and F30 have a very strong joint positive contribution, increasing the classifier's response higher in favour of C2 than C1. In contrast, F24, F6, and F33 are the top negative features, degrading the classifier's response in favour of C1. Comparing the attributions of F10, F36, and F30 to those of the negative features mentioned above, it is not surprising that the classifier is quite confident that C2 is the most probable label here.

{'C1': '3.10%', 'C2': '96.90%'}

{'F10': 'Unique Received From Addresses', 'F30': ' ERC20 total Ether sent contract', 'F24': 'total ether received', 'F33': 'Sent tnx', 'F6': 'Number of Created Contracts', 'F36': ' ERC20 uniq rec token name', 'F28': ' ERC20 uniq rec contract addr', 'F23': 'max value received ', 'F34': 'total transactions (including tnx to create contract', 'F5': ' ERC20 uniq sent addr.1', 'F8': ' ERC20 uniq sent addr', 'F15': 'Received Tnx', 'F1': 'avg val received', 'F22': ' ERC20 uniq rec addr', 'F25': 'avg val sent', 'F38': 'min value received', 'F7': 'Unique Sent To Addresses', 'F37': ' ERC20 uniq sent token name', 'F12': 'Avg min between received tnx', 'F29': 'Time Diff between first and last (Mins)', 'F11': ' ERC20 min val rec', 'F32': ' ERC20 max val rec', 'F14': ' ERC20 min val sent', 'F9': ' ERC20 max val sent', 'F31': ' ERC20 avg val sent', 'F35': ' ERC20 avg val rec', 'F3': ' Total ERC20 tnxs', 'F16': ' ERC20 total ether sent', 'F4': ' ERC20 total Ether received', 'F2': 'total ether balance', 'F20': 'total ether sent contracts', 'F18': 'total Ether sent', 'F17': 'avg value sent to contract', 'F21': 'max val sent to contract', 'F26': 'min value sent to contract', 'F13': 'max val sent', 'F27': 'min val sent', 'F19': 'Avg min between sent tnx'}

{'F7': 'F10', 'F26': 'F30', 'F20': 'F24', 'F4': 'F33', 'F6': 'F6', 'F38': 'F36', 'F30': 'F28', 'F10': 'F23', 'F18': 'F34', 'F29': 'F5', 'F27': 'F8', 'F5': 'F15', 'F11': 'F1', 'F28': 'F22', 'F14': 'F25', 'F9': 'F38', 'F8': 'F7', 'F37': 'F37', 'F2': 'F12', 'F3': 'F29', 'F31': 'F11', 'F32': 'F32', 'F34': 'F14', 'F35': 'F9', 'F36': 'F31', 'F33': 'F35', 'F23': 'F3', 'F25': 'F16', 'F24': 'F4', 'F22': 'F2', 'F21': 'F20', 'F19': 'F18', 'F17': 'F17', 'F16': 'F21', 'F15': 'F26', 'F13': 'F13', 'F12': 'F27', 'F1': 'F19'}

{'C1': 'C1', 'C2': 'C2'}

Fraud

{'C1': 'Not Fraud', 'C2': 'Fraud'}

RandomForestClassifier

C1

Printer Sales

There is only a 17.0% chance that C2 is the correct label which implies that the most probable label for the given data or case is C1 given its predicted likelihood of 83.0%. The main influential features resulting in the classification conclusions above are F3, F7, and F26 whereas the remaining features have either a moderate or negligible influence on the classifier. When it comes to assigning a label to this case, the classifier likely ignored the values of F1, F10, F17, F14, F18, and F22 since their respective degrees of influence are very close to zero. Among the influential features, only F13, F25, F2, F19, F23, and F24 are considered negative features mainly due to the fact that their contributions towards the decision here only serve to decrease the likelihood that C1 is the correct label and it can be said that these features favour labelling the case as C2. The remaining features such as F3, F7, F26, F15, F6, F21, and F5, offer positive contributions, increasing the likelihood of the C1 class.

{'C1': '83.00%', 'C2': '17.00%'}

{'F3': 'X8', 'F7': 'X24', 'F26': 'X1', 'F15': 'X2', 'F6': 'X10', 'F21': 'X15', 'F13': 'X25', 'F5': 'X23', 'F16': 'X18', 'F25': 'X4', 'F2': 'X7', 'F20': 'X17', 'F12': 'X3', 'F9': 'X22', 'F19': 'X5', 'F23': 'X9', 'F24': 'X12', 'F11': 'X19', 'F8': 'X11', 'F4': 'X16', 'F1': 'X14', 'F10': 'X21', 'F17': 'X20', 'F14': 'X13', 'F18': 'X6', 'F22': 'X26'}

{'F8': 'F3', 'F24': 'F7', 'F1': 'F26', 'F2': 'F15', 'F10': 'F6', 'F15': 'F21', 'F25': 'F13', 'F23': 'F5', 'F18': 'F16', 'F4': 'F25', 'F7': 'F2', 'F17': 'F20', 'F3': 'F12', 'F22': 'F9', 'F5': 'F19', 'F9': 'F23', 'F12': 'F24', 'F19': 'F11', 'F11': 'F8', 'F16': 'F4', 'F14': 'F1', 'F21': 'F10', 'F20': 'F17', 'F13': 'F14', 'F6': 'F18', 'F26': 'F22'}

{'C2': 'C1', 'C1': 'C2'}

Less

{'C1': 'Less', 'C2': 'More'}

RandomForestClassifier

C2

Student Job Placement

The classification algorithm's decision on the true label for the given case is solely dependent on the information presented to it. Per the algorithm, the accurate label for the case under consideration is most likely C2, and the 12.47% possibility of C1 reflects only a minor uncertainty in the classification algorithm's certainty. The marginal doubt mentioned above can be blamed on the negative contributions of F12, F10, F1, F2, and F11, supporting the assignment of C1 instead of C2. Conversely, the positive contributions of F7, F3, F8, F5, F6, and F4 are shifting the algorithm's decision higher in favour of label C2, hence the high certainty of its correctness. Overall, F12 and F10 are the most influential negative features, whereas F7 and F3 are the most positive features. Also, F9 is shown to have a negligible influence on the classification decision with respect to the case here.

{'C1': '12.47%', 'C2': '87.53%'}

{'F12': 'workex', 'F10': 'specialisation', 'F7': 'hsc_p', 'F3': 'gender', 'F8': 'mba_p', 'F1': 'hsc_s', 'F5': 'ssc_p', 'F6': 'etest_p', 'F2': 'ssc_b', 'F4': 'hsc_b', 'F11': 'degree_t', 'F9': 'degree_p'}

{'F11': 'F12', 'F12': 'F10', 'F2': 'F7', 'F6': 'F3', 'F5': 'F8', 'F9': 'F1', 'F1': 'F5', 'F4': 'F6', 'F7': 'F2', 'F8': 'F4', 'F10': 'F11', 'F3': 'F9'}

{'C2': 'C1', 'C1': 'C2'}

Placed

{'C1': 'Not Placed', 'C2': 'Placed'}

KNeighborsClassifier

C2

Printer Sales

The model indicates that the label for this case is likely C2, with an 83.33% chance that it is correct, implying that it is unlikely that C1 is the appropriate class. This predictive assertion is chiefly influenced by the values of the input variables F22, F23, and F5. While the F5 and F23 values positively control the model towards the prediction of C2, the F22 value biases the decision towards C1. However, the combined effect of F5 and F23 outweighs the contribution of F22. In addition, the variables F2, F7, and F24 also positively support the output predictions of the model. F9 has similar direction of contribution that of F22, further decreasing the odds of the C2 label. Unlike all the variables above, F14, F6, F8, F1, F11, and F21 are shown to have very little effect on model predictions with respect to the given case and we can say that their values receive very low consideration from the model.

{'C2': '83.33%', 'C1': '16.67%'}

{'F5': 'X24', 'F23': 'X1', 'F22': 'X4', 'F2': 'X10', 'F7': 'X2', 'F24': 'X8', 'F9': 'X17', 'F15': 'X7', 'F3': 'X21', 'F16': 'X18', 'F20': 'X6', 'F19': 'X11', 'F26': 'X22', 'F10': 'X25', 'F17': 'X5', 'F12': 'X19', 'F4': 'X15', 'F13': 'X23', 'F25': 'X16', 'F18': 'X3', 'F14': 'X14', 'F6': 'X20', 'F1': 'X13', 'F8': 'X12', 'F11': 'X9', 'F21': 'X26'}

{'F24': 'F5', 'F1': 'F23', 'F4': 'F22', 'F10': 'F2', 'F2': 'F7', 'F8': 'F24', 'F17': 'F9', 'F7': 'F15', 'F21': 'F3', 'F18': 'F16', 'F6': 'F20', 'F11': 'F19', 'F22': 'F26', 'F25': 'F10', 'F5': 'F17', 'F19': 'F12', 'F15': 'F4', 'F23': 'F13', 'F16': 'F25', 'F3': 'F18', 'F14': 'F14', 'F20': 'F6', 'F13': 'F1', 'F12': 'F8', 'F9': 'F11', 'F26': 'F21'}

{'C2': 'C2', 'C1': 'C1'}

Less

{'C2': 'Less', 'C1': 'More'}

LogisticRegression

C1

Hotel Satisfaction

The algorithm's forecast for the data instance under consideration is C1, and the decision's confidence level is about 91.36 percent. We can observe from the plot that the variables F9 and F8 are moving the prediction judgement towards the other label, C2. The F14, F5, F15, and F11, on the other hand, have values that have a favourable influence, pushing the data classification choice towards label C1. While F7 and F6 contradict the prediction, F1 and F2 have values that confirm the algorithm's prediction output verdict.

{'C1': '91.36%', 'C2': '8.64%'}

{'F9': 'Type of Travel', 'F8': 'Type Of Booking', 'F14': 'Hotel wifi service', 'F5': 'Common Room entertainment', 'F15': 'Stay comfort', 'F11': 'Other service', 'F7': 'Checkin\\/Checkout service', 'F1': 'Hotel location', 'F6': 'Food and drink', 'F2': 'Cleanliness', 'F4': 'Age', 'F10': 'Departure\\/Arrival convenience', 'F3': 'purpose_of_travel', 'F12': 'Ease of Online booking', 'F13': 'Gender'}

{'F3': 'F9', 'F4': 'F8', 'F6': 'F14', 'F12': 'F5', 'F11': 'F15', 'F14': 'F11', 'F13': 'F7', 'F9': 'F1', 'F10': 'F6', 'F15': 'F2', 'F5': 'F4', 'F7': 'F10', 'F2': 'F3', 'F8': 'F12', 'F1': 'F13'}

{'C2': 'C1', 'C1': 'C2'}

dissatisfied

{'C1': 'dissatisfied', 'C2': 'satisfied'}

SVC

C2

Vehicle Insurance Claims

The model classifies this case as C2 and it is noteworthy that there is, however, a 38.26% chance that the true label could be class C1. The uncertainty associated with the classification decision above is higher than expected, which could be attributed to the values of the different input features. The most influential feature is F26, which has a positive effect on the class C2 prediction by the model here. All other features are much less influential, with contributions from F30, F27, F12, and F25 shifting the prediction towards C1. Supporting the model in assigning the label choice, F13 is the next most influential feature. The impacts of the F18 and F15 are moderate, ranking seventh and eighth, respectively. Unfortunately, values of features such as F28, F9, F22, and F1 do not matter when determining the correct label in this instance.

{'C2': '61.74%', 'C1': '38.26%'}

{'F26': 'incident_severity', 'F30': 'insured_hobbies', 'F27': 'insured_occupation', 'F12': 'umbrella_limit', 'F25': 'policy_csl', 'F13': 'authorities_contacted', 'F18': 'insured_education_level', 'F15': 'collision_type', 'F23': 'months_as_customer', 'F6': 'vehicle_claim', 'F19': 'insured_relationship', 'F33': 'capital-gains', 'F5': 'auto_make', 'F24': 'injury_claim', 'F11': 'incident_city', 'F29': 'insured_sex', 'F2': 'number_of_vehicles_involved', 'F3': 'incident_hour_of_the_day', 'F32': 'age', 'F7': 'property_claim', 'F28': 'policy_annual_premium', 'F9': 'police_report_available', 'F22': 'property_damage', 'F1': 'incident_state', 'F16': 'policy_deductable', 'F21': 'capital-loss', 'F10': 'insured_zip', 'F14': 'incident_type', 'F17': 'bodily_injuries', 'F4': 'witnesses', 'F8': 'policy_state', 'F31': 'total_claim_amount', 'F20': 'auto_year'}

{'F27': 'F26', 'F23': 'F30', 'F22': 'F27', 'F5': 'F12', 'F19': 'F25', 'F28': 'F13', 'F21': 'F18', 'F26': 'F15', 'F1': 'F23', 'F16': 'F6', 'F24': 'F19', 'F7': 'F33', 'F33': 'F5', 'F14': 'F24', 'F30': 'F11', 'F20': 'F29', 'F10': 'F2', 'F9': 'F3', 'F2': 'F32', 'F15': 'F7', 'F4': 'F28', 'F32': 'F9', 'F31': 'F22', 'F29': 'F1', 'F3': 'F16', 'F8': 'F21', 'F6': 'F10', 'F25': 'F14', 'F11': 'F17', 'F12': 'F4', 'F18': 'F8', 'F13': 'F31', 'F17': 'F20'}

{'C1': 'C2', 'C2': 'C1'}

Fraud

{'C2': 'Fraud', 'C1': 'Not Fraud'}

SVM_linear

C1

Wine Quality Prediction

The classification or prediction algorithm indicates that the most probable label for the given data is C1 since there is only a 25.47% chance that C2 could be the correct label. The major factors resulting in the above decision are F7, F9, and F3, while the set of features with moderate influence are F8, F5, F2, and F10. The least vital features are shown to be F1, F4, F6, and F11. In conclusion, it is very surprising to see the uncertainty surrounding the classification here given that only F8 and F2 have a negative impact, driving the algorithm to label the data as C2. To be specific, the contributions of F8 and F2 result in a decrease in the likelihood of C1 being the right label, as indicated by the prediction probabilities across the two possible classes but the influence of these negatives are moderated by the major positive features which are F7, F9, and F3.

{'C2': '25.47%', 'C1': '74.53%'}

{'F7': 'sulphates', 'F9': 'volatile acidity', 'F3': 'total sulfur dioxide', 'F8': 'residual sugar', 'F5': 'alcohol', 'F2': 'free sulfur dioxide', 'F10': 'chlorides', 'F1': 'fixed acidity', 'F4': 'citric acid', 'F6': 'pH', 'F11': 'density'}

{'F10': 'F7', 'F2': 'F9', 'F7': 'F3', 'F4': 'F8', 'F11': 'F5', 'F6': 'F2', 'F5': 'F10', 'F1': 'F1', 'F3': 'F4', 'F9': 'F6', 'F8': 'F11'}

{'C2': 'C2', 'C1': 'C1'}

high quality

{'C2': 'low_quality', 'C1': 'high quality'}

RandomForestClassifier

C2

Flight Price-Range Classification

The classification verdict is as follows: the most probable label for this case is C2, and the classifier is certain that neither C1 nor C3 is the correct label. The main drivers for the above classification are F6, F11, and F4, all of which have a strong positive influence, pushing the classifier to choose C2. Other positive features pushing the classification further higher towards C2 include F12, F8, F3, and F5. Not all the input features support the assigned label and the negative features F9, F7, and F10 indicate that the most probable class for this case could different from the assigned label. However, considering the confidence level in the above classification, it is valid to conclude that the classifier paid little attention to the negative features, hence selecting class C2.

{'C2': '100.00%', 'C1': '0.00%', 'C3': '0.00%'}

{'F6': 'Airline', 'F11': 'Duration_hours', 'F4': 'Total_Stops', 'F12': 'Journey_month', 'F7': 'Source', 'F8': 'Destination', 'F3': 'Arrival_hour', 'F9': 'Journey_day', 'F10': 'Dep_minute', 'F5': 'Arrival_minute', 'F2': 'Duration_mins', 'F1': 'Dep_hour'}

{'F9': 'F6', 'F7': 'F11', 'F12': 'F4', 'F2': 'F12', 'F10': 'F7', 'F11': 'F8', 'F5': 'F3', 'F1': 'F9', 'F4': 'F10', 'F6': 'F5', 'F8': 'F2', 'F3': 'F1'}

{'C1': 'C2', 'C2': 'C1', 'C3': 'C3'}

Low

{'C2': 'Low', 'C1': 'Moderate', 'C3': 'High'}

RandomForestClassifier

C1

Ethereum Fraud Detection

The best choice of label for the given case is C1 according to the classification algorithm, since there is little to no chance that C2 is the right class. Not all the features are shown to contribute either positively or negatively towards the label assigned here. The influential features can be ranked according to the associated degree of impact on the algorithm's output as follows: F21, F28, F34, F26, F12, F16, F36, F13, F5, F35, F33, F30, F27, F29, F4, F10, F14, F31, F17, F37. On the other hand, the irrelevant features include F22, F25, and F32 since they have close to zero impact. Among the top influential ones, F21, F28, F34, F26, and F12, the input feature F34 is regarded as the most negative, dragging the verdict in a different direction, while the others have positive contributions, improving the likelihood that the choice of C1 is appropriate in this case. The features with moderate influence are F36, F16, F13 where F36 is identified as a positive feature, while F16 and F13 considered negative features. Since a large number of top features have positive contributions that increase the probability that C1 is the right label, it is not surprising that the algorithm is very confident about the correctness of the assigned label.

{'C2': '0.00%', 'C1': '100.00%'}

{'F21': ' ERC20 total Ether sent contract', 'F28': ' ERC20 min val rec', 'F34': 'total transactions (including tnx to create contract', 'F26': ' ERC20 max val rec', 'F12': ' Total ERC20 tnxs', 'F16': ' ERC20 uniq rec addr', 'F36': 'min val sent', 'F13': 'Time Diff between first and last (Mins)', 'F5': 'Sent tnx', 'F35': 'Avg min between received tnx', 'F33': 'min value received', 'F30': ' ERC20 total ether sent', 'F27': 'avg val sent', 'F29': 'max val sent', 'F4': 'Avg min between sent tnx', 'F10': 'Received Tnx', 'F14': ' ERC20 uniq sent token name', 'F31': 'Unique Sent To Addresses', 'F17': ' ERC20 uniq rec token name', 'F37': ' ERC20 uniq rec contract addr', 'F22': 'total Ether sent', 'F25': 'Number of Created Contracts', 'F32': ' ERC20 avg val sent', 'F3': ' ERC20 max val sent', 'F19': ' ERC20 min val sent', 'F18': ' ERC20 avg val rec', 'F9': 'Unique Received From Addresses', 'F20': 'max value received ', 'F23': ' ERC20 uniq sent addr.1', 'F7': 'total ether sent contracts', 'F2': 'avg val received', 'F38': ' ERC20 uniq sent addr', 'F15': 'min value sent to contract', 'F1': 'max val sent to contract', 'F11': ' ERC20 total Ether received', 'F6': 'avg value sent to contract', 'F8': 'total ether balance', 'F24': 'total ether received'}

{'F26': 'F21', 'F31': 'F28', 'F18': 'F34', 'F32': 'F26', 'F23': 'F12', 'F28': 'F16', 'F12': 'F36', 'F3': 'F13', 'F4': 'F5', 'F2': 'F35', 'F9': 'F33', 'F25': 'F30', 'F14': 'F27', 'F13': 'F29', 'F1': 'F4', 'F5': 'F10', 'F37': 'F14', 'F8': 'F31', 'F38': 'F17', 'F30': 'F37', 'F19': 'F22', 'F6': 'F25', 'F36': 'F32', 'F35': 'F3', 'F34': 'F19', 'F33': 'F18', 'F7': 'F9', 'F10': 'F20', 'F29': 'F23', 'F21': 'F7', 'F11': 'F2', 'F27': 'F38', 'F15': 'F15', 'F16': 'F1', 'F24': 'F11', 'F17': 'F6', 'F22': 'F8', 'F20': 'F24'}

{'C1': 'C2', 'C2': 'C1'}

Fraud

{'C2': 'Not Fraud', 'C1': 'Fraud'}

LogisticRegression

C2

Student Job Placement

For the given case, the prediction decision is as follows: The probability of C1 being the correct label is only 18.57%, the probability of C2 is 81.43% making it the most probable label for the case here. The certainty of the prediction can be attributed to the influence of variables such as F1, F9, F11, F3, and F5. The least relevant variables considered to arrive at the classification verdict are F8, F4, F7, and F6. F10, F12, and F2 have moderate contributions to the classification here. The attribution analysis performed indicates that F3, F5, F12, F2, F4, and F6 are the negative variables, decreasing the likelihood of C2 in favour of labelling the given case as C1. The variables F1, F9, and F11 have the highest positive influence, which increases the odds of label C2 being the correct label.

{'C1': '18.57%', 'C2': '81.43%'}

{'F1': 'mba_p', 'F9': 'gender', 'F11': 'degree_t', 'F3': 'specialisation', 'F5': 'workex', 'F10': 'hsc_s', 'F12': 'hsc_p', 'F2': 'ssc_p', 'F8': 'ssc_b', 'F4': 'etest_p', 'F7': 'hsc_b', 'F6': 'degree_p'}

{'F5': 'F1', 'F6': 'F9', 'F10': 'F11', 'F12': 'F3', 'F11': 'F5', 'F9': 'F10', 'F2': 'F12', 'F1': 'F2', 'F7': 'F8', 'F4': 'F4', 'F8': 'F7', 'F3': 'F6'}

{'C2': 'C1', 'C1': 'C2'}

Placed

{'C1': 'Not Placed', 'C2': 'Placed'}

SGDClassifier

C1

Flight Price-Range Classification

The output decision of the classifier with respect to the given case is: C1 is the most probable label, followed by C3 and C2. To be specific, the predicted likelihood across the classes are as follows: 86.54% for C1, 13.46% for C3, and finally a 0.0% probability with respect to C2. The moderately high classification confidence could largely be due to the impact of certain input features supplied to the classifier. F3, F2, F4, F8, and F5 are the top-ranked variables whereas the least ranked are F12, F6, F9, F1, F10, F11, and F7. The marginal uncertainty in the classification verdict is due to the negative attributions of F2, F5, F9, F10, and F7 which prefer labelling the case differently. In conclusion, we can see that F3, F4, F8, F6, and F12 are among the positive variables pushing the classification in favour of C1.

{'C1': '86.54%', 'C3': '13.46%', 'C2': '0.00%'}

{'F3': 'Airline', 'F2': 'Total_Stops', 'F4': 'Source', 'F8': 'Journey_month', 'F5': 'Arrival_minute', 'F12': 'Journey_day', 'F6': 'Duration_hours', 'F9': 'Dep_hour', 'F1': 'Destination', 'F10': 'Arrival_hour', 'F11': 'Dep_minute', 'F7': 'Duration_mins'}

{'F9': 'F3', 'F12': 'F2', 'F10': 'F4', 'F2': 'F8', 'F6': 'F5', 'F1': 'F12', 'F7': 'F6', 'F3': 'F9', 'F11': 'F1', 'F5': 'F10', 'F4': 'F11', 'F8': 'F7'}

{'C1': 'C1', 'C2': 'C3', 'C3': 'C2'}

Low

{'C1': 'Low', 'C3': 'Moderate', 'C2': 'High'}

LogisticRegression

C1

Airline Passenger Satisfaction

C1 is the predicted label assigned to this case or instance. This is based on the fact that there is only a 0.68% chance that C2 is the correct label. The most relevant variables that increase the prediction's probability are F22, F12, F1, and F20. Conversely, F6 is the only important feature driving the classification decision in the direction of C2. Other negative features include F17, F4, F19, and F21. Other positive features increasing the chances of the C1 prediction are F16, F8, and F13. Unlike F22, F12, F1, and F20, these positive variables have moderate contributions to the model's decision. The least ranked among all the relevant features are F7, F14, F18, and F9, with lower attributions to the C1 prediction, however, F15 and F10 are shown to have no impact when determining the correct label for the case under consideration.

{'C1': '99.32%', 'C2': '0.68%'}

{'F22': 'Type of Travel', 'F6': 'Customer Type', 'F12': 'Inflight entertainment', 'F20': 'Inflight wifi service', 'F1': 'Departure\\/Arrival time convenient', 'F17': 'Gate location', 'F4': 'Arrival Delay in Minutes', 'F19': 'Seat comfort', 'F21': 'Online boarding', 'F16': 'Ease of Online booking', 'F8': 'Class', 'F13': 'Age', 'F2': 'On-board service', 'F5': 'Cleanliness', 'F3': 'Checkin service', 'F11': 'Inflight service', 'F7': 'Food and drink', 'F14': 'Departure Delay in Minutes', 'F18': 'Baggage handling', 'F9': 'Gender', 'F15': 'Flight Distance', 'F10': 'Leg room service'}

{'F4': 'F22', 'F2': 'F6', 'F14': 'F12', 'F7': 'F20', 'F8': 'F1', 'F10': 'F17', 'F22': 'F4', 'F13': 'F19', 'F12': 'F21', 'F9': 'F16', 'F5': 'F8', 'F3': 'F13', 'F15': 'F2', 'F20': 'F5', 'F18': 'F3', 'F19': 'F11', 'F11': 'F7', 'F21': 'F14', 'F17': 'F18', 'F1': 'F9', 'F6': 'F15', 'F16': 'F10'}

{'C2': 'C1', 'C1': 'C2'}

neutral or dissatisfied

{'C1': 'neutral or dissatisfied', 'C2': 'satisfied'}

RandomForestClassifier

C2

Personal Loan Modelling

The following classification decisions are largely based on the factors or attributes of this particular case. The class label, in this case, is projected to be C2 out of the potential classes, which is 97.50% likely. The next possible label is C1, which has an approximate probability of 2.50%. The confidence level with respect to this classification is very high, and the features with the most contributions are F9, F7, and F3. However, F5, F8, and F2 are shown to be the least relevant features. The attribution analysis shows that the only positive features whose contributions favour labelling the case as C2 are F7, F3, F6, and F4. However, the negative attributions of F9, F1, F8, F5, and F2 also indicate that perhaps C1 could be the true label. Judging based on the confidence level coupled with the attributions, it can be concluded that the values of the positive features F7, F3, F6, and F4 are good enough to steer the classification in the direction of C2, but the strong negative attribution of F9 casts about 2.50% of doubt on the decision.

{'C2': '97.50%', 'C1': '2.50%'}

{'F9': 'Income', 'F7': 'CD Account', 'F3': 'Education', 'F1': 'Securities Account', 'F6': 'CCAvg', 'F4': 'Family', 'F8': 'Extra_service', 'F5': 'Age', 'F2': 'Mortgage'}

{'F2': 'F9', 'F8': 'F7', 'F5': 'F3', 'F7': 'F1', 'F4': 'F6', 'F3': 'F4', 'F9': 'F8', 'F1': 'F5', 'F6': 'F2'}

{'C1': 'C2', 'C2': 'C1'}

Reject

{'C2': 'Reject', 'C1': 'Accept'}

DNN

C1

Ethereum Fraud Detection

The prediction probabilities for classes C2 and C1, respectively, are 15.35% and 84.65%. Based on the aforementioned, C1 is the most likely class label for the presented data instance, and according to the attribution analysis, the various input variables had varying degrees of impact on the model's classification judgement. F29, F22, F28, F19, F27, F11, and F10 are the most influential factors, whereas F1, F31, F12, F32, F16, and F36 have the least impact. The subsequent analysis will concentrate on the most relevant factors influencing the label selection in this case. Looking at the attributions of the input features, only F29 and F22 exhibit negative contributions among the top influential features, F29, F22, F28, F19, and F10, lowering the chance that C1 is the right label, and they strongly favour labelling the instance as C2 instead. Positive variables such as F28, F19, and F10 influence the classification choice in favour of C1. The remaining variables, including F27, F11, and F34, have a moderate to low impact. In essence, the marginal uncertainty in this decision is mostly owing to the negative impacts of F29, F22, F2, and F35, while the positive contributions of F28, F19, F34, F27, F11, and F10 push the decision much closer to C1.

{'C2': '15.35%', 'C1': '84.65%'}

{'F29': ' ERC20 uniq rec contract addr', 'F22': ' ERC20 uniq rec token name', 'F28': 'min value received', 'F19': 'Time Diff between first and last (Mins)', 'F10': 'avg val sent', 'F27': ' ERC20 uniq sent token name', 'F11': 'Sent tnx', 'F34': 'Avg min between received tnx', 'F2': 'Unique Received From Addresses', 'F35': ' ERC20 uniq rec addr', 'F7': 'total transactions (including tnx to create contract', 'F37': 'Avg min between sent tnx', 'F24': ' ERC20 uniq sent addr.1', 'F9': 'avg val received', 'F5': 'Unique Sent To Addresses', 'F20': 'max value received ', 'F13': 'max val sent', 'F4': 'min val sent', 'F8': 'Number of Created Contracts', 'F33': 'total ether received', 'F38': ' ERC20 uniq sent addr', 'F3': ' ERC20 total Ether received', 'F15': 'Received Tnx', 'F30': ' ERC20 avg val sent', 'F17': 'total Ether sent', 'F21': ' ERC20 min val sent', 'F25': 'max val sent to contract', 'F23': 'total ether balance', 'F6': ' ERC20 max val sent', 'F18': ' Total ERC20 tnxs', 'F14': ' ERC20 total ether sent', 'F26': ' ERC20 avg val rec', 'F12': 'avg value sent to contract', 'F31': ' ERC20 min val rec', 'F1': ' ERC20 max val rec', 'F36': ' ERC20 total Ether sent contract', 'F16': 'min value sent to contract', 'F32': 'total ether sent contracts'}

{'F30': 'F29', 'F38': 'F22', 'F9': 'F28', 'F3': 'F19', 'F14': 'F10', 'F37': 'F27', 'F4': 'F11', 'F2': 'F34', 'F7': 'F2', 'F28': 'F35', 'F18': 'F7', 'F1': 'F37', 'F29': 'F24', 'F11': 'F9', 'F8': 'F5', 'F10': 'F20', 'F13': 'F13', 'F12': 'F4', 'F6': 'F8', 'F20': 'F33', 'F27': 'F38', 'F24': 'F3', 'F5': 'F15', 'F36': 'F30', 'F19': 'F17', 'F34': 'F21', 'F16': 'F25', 'F22': 'F23', 'F35': 'F6', 'F23': 'F18', 'F25': 'F14', 'F33': 'F26', 'F17': 'F12', 'F31': 'F31', 'F32': 'F1', 'F26': 'F36', 'F15': 'F16', 'F21': 'F32'}

{'C2': 'C2', 'C1': 'C1'}

Fraud

{'C2': 'Not Fraud', 'C1': 'Fraud'}

RandomForestClassifier

C2

Company Bankruptcy Prediction

The model assigns the class C2 with near perfect certainty or confidence level since the predicted likelihood of C1 is only 1.0%. F40, F57, F10, F87, and F63 have the greatest cumulative beneficial influence on the model's choice to create C2. F17 also had a significant influence, but it shifted the choice away from C2. Furthermore, F37 and F42 had a modest influence on C2 decision making, which was still bigger than features F16 and F33, which had a moderate impact and contributed to C1 class prediction. Furthermore, F42, F26, and F38 have minimal positive impact on the final result, further increasing the chances of C2 being the appropriate label for the given case. However, a number of input features, notably F28, F69, F80, and F24, appear to be less essential to predictions here. All in all, the very high confidence level could easily be explained away by considering the fact that the joint influence of the positive variables such as F40, F57, F10, F87, and F63 far outshines the joint contribution of the negative variables such as F17, F16, F33, and F56.

{'C2': '99.00%', 'C1': '1.00%'}

{'F40': " Net Income to Stockholder's Equity", 'F10': ' Continuous interest rate (after tax)', 'F87': ' ROA(C) before interest and depreciation before interest', 'F17': ' Borrowing dependency', 'F57': ' Cash Flow Per Share', 'F63': ' Net worth\\/Assets', 'F37': ' Total income\\/Total expense', 'F42': ' Persistent EPS in the Last Four Seasons', 'F26': ' Retained Earnings to Total Assets', 'F38': ' Net Value Per Share (B)', 'F44': ' Cash Flow to Equity', 'F7': ' Net Value Per Share (A)', 'F64': ' Degree of Financial Leverage (DFL)', 'F16': ' Per Share Net profit before tax (Yuan ¥)', 'F33': ' Revenue Per Share (Yuan ¥)', 'F56': ' Inventory Turnover Rate (times)', 'F11': ' Net profit before tax\\/Paid-in capital', 'F49': ' Equity to Long-term Liability', 'F61': ' Operating profit\\/Paid-in capital', 'F43': ' Cash Turnover Rate', 'F28': ' Operating Funds to Liability', 'F69': ' Contingent liabilities\\/Net worth', 'F80': ' Working Capital to Total Assets', 'F24': ' Liability to Equity', 'F93': ' Current Liability to Liability', 'F6': ' Operating Gross Margin', 'F89': ' Operating Profit Per Share (Yuan ¥)', 'F62': ' Long-term Liability to Current Assets', 'F71': ' Current Asset Turnover Rate', 'F23': ' Interest Coverage Ratio (Interest expense to EBIT)', 'F34': ' Equity to Liability', 'F48': ' Operating Profit Rate', 'F78': ' Current Liability to Equity', 'F52': ' No-credit Interval', 'F66': ' Net Worth Turnover Rate (times)', 'F72': ' Working Capital\\/Equity', 'F20': ' Quick Assets\\/Current Liability', 'F73': ' Inventory and accounts receivable\\/Net value', 'F50': ' Current Liability to Current Assets', 'F60': ' Working capitcal Turnover Rate', 'F9': ' Fixed Assets to Assets', 'F12': ' Continuous Net Profit Growth Rate', 'F46': ' Cash Reinvestment %', 'F68': ' CFO to Assets', 'F82': ' Total Asset Turnover', 'F22': ' After-tax net Interest Rate', 'F27': ' After-tax Net Profit Growth Rate', 'F55': ' Tax rate (A)', 'F90': ' Current Ratio', 'F39': ' Realized Sales Gross Margin', 'F3': ' Net Value Per Share (C)', 'F5': ' Regular Net Profit Growth Rate', 'F31': ' Interest-bearing debt interest rate', 'F29': ' Debt ratio %', 'F21': ' Long-term fund suitability ratio (A)', 'F74': ' Net Value Growth Rate', 'F51': ' Total Asset Growth Rate', 'F75': ' Fixed Assets Turnover Frequency', 'F53': ' Inventory\\/Current Liability', 'F15': ' Allocation rate per person', 'F83': ' Operating Expense Rate', 'F65': ' Operating profit per person', 'F54': ' Net Income to Total Assets', 'F81': ' Interest Expense Ratio', 'F35': ' Cash\\/Total Assets', 'F58': ' ROA(B) before interest and depreciation after tax', 'F8': ' Inventory\\/Working Capital', 'F4': ' Total assets to GNP price', 'F76': ' Total debt\\/Total net worth', 'F79': ' Quick Ratio', 'F59': ' Revenue per person', 'F88': ' Non-industry income and expenditure\\/revenue', 'F18': ' Cash Flow to Sales', 'F41': ' ROA(A) before interest and % after tax', 'F45': ' Current Liabilities\\/Liability', 'F1': ' Operating Profit Growth Rate', 'F85': ' Cash Flow to Liability', 'F86': ' Cash Flow to Total Assets', 'F67': ' Pre-tax net Interest Rate', 'F14': ' Accounts Receivable Turnover', 'F36': ' Current Liability to Assets', 'F70': ' Quick Assets\\/Total Assets', 'F19': ' Total expense\\/Assets', 'F30': ' Average Collection Days', 'F13': ' Research and development expense rate', 'F32': ' Current Assets\\/Total Assets', 'F84': ' Current Liabilities\\/Equity', 'F92': ' Realized Sales Gross Profit Growth Rate', 'F25': ' Cash flow rate', 'F77': ' Total Asset Return Growth Rate Ratio', 'F47': ' Quick Asset Turnover Rate', 'F2': ' Cash\\/Current Liability', 'F91': ' Gross Profit to Sales'}

{'F59': 'F40', 'F12': 'F10', 'F29': 'F87', 'F3': 'F17', 'F65': 'F57', 'F84': 'F63', 'F57': 'F37', 'F8': 'F42', 'F10': 'F26', 'F27': 'F38', 'F53': 'F44', 'F42': 'F7', 'F35': 'F64', 'F78': 'F16', 'F31': 'F33', 'F18': 'F56', 'F72': 'F11', 'F23': 'F49', 'F89': 'F61', 'F34': 'F43', 'F87': 'F28', 'F64': 'F69', 'F67': 'F80', 'F66': 'F24', 'F90': 'F93', 'F62': 'F6', 'F63': 'F89', 'F69': 'F62', 'F61': 'F71', 'F60': 'F23', 'F91': 'F34', 'F58': 'F48', 'F92': 'F78', 'F56': 'F52', 'F55': 'F66', 'F68': 'F72', 'F71': 'F20', 'F70': 'F73', 'F86': 'F50', 'F73': 'F60', 'F74': 'F9', 'F54': 'F12', 'F75': 'F46', 'F76': 'F68', 'F77': 'F82', 'F79': 'F22', 'F80': 'F27', 'F81': 'F55', 'F82': 'F90', 'F83': 'F39', 'F88': 'F3', 'F85': 'F5', 'F1': 'F31', 'F47': 'F29', 'F52': 'F21', 'F15': 'F74', 'F24': 'F51', 'F22': 'F75', 'F21': 'F53', 'F20': 'F15', 'F19': 'F83', 'F17': 'F65', 'F16': 'F54', 'F14': 'F81', 'F26': 'F35', 'F13': 'F58', 'F11': 'F8', 'F9': 'F4', 'F7': 'F76', 'F6': 'F79', 'F5': 'F59', 'F4': 'F88', 'F25': 'F18', 'F28': 'F41', 'F51': 'F45', 'F43': 'F1', 'F50': 'F85', 'F49': 'F86', 'F48': 'F67', 'F2': 'F14', 'F46': 'F36', 'F45': 'F70', 'F44': 'F19', 'F41': 'F30', 'F30': 'F13', 'F40': 'F32', 'F39': 'F84', 'F38': 'F92', 'F37': 'F25', 'F36': 'F77', 'F33': 'F47', 'F32': 'F2', 'F93': 'F91'}

{'C1': 'C2', 'C2': 'C1'}

No

{'C2': 'No', 'C1': 'Yes'}

RandomForestClassifier

C1

House Price Classification

Between the two classes, the model labelled this case as C1 with a likelihood of about 97.0% since there is only a marginal chance that it belongs to label C2. The most relevant features influencing this decision are F5, F10, F12, and F3. In this case, F5, F10, and F3 have a considerable positive influence on the prediction of C1. In contrast, the values of F12 and F6 throw a bit of doubt on the C1 prediction. However, compared to F5, F10, and F3, this shift is very small. Finally, there are some attributes with limited impact on the prediction of C1 and these are F8, F1, F7, F9, F2, and F11 since their values are less important to the model in terms of determining the label for this case.

{'C2': '3.00%', 'C1': '97.00%'}

{'F5': 'LSTAT', 'F10': 'RM', 'F3': 'AGE', 'F12': 'TAX', 'F4': 'PTRATIO', 'F6': 'DIS', 'F13': 'CRIM', 'F8': 'RAD', 'F1': 'B', 'F7': 'NOX', 'F9': 'ZN', 'F2': 'INDUS', 'F11': 'CHAS'}

{'F13': 'F5', 'F6': 'F10', 'F7': 'F3', 'F10': 'F12', 'F11': 'F4', 'F8': 'F6', 'F1': 'F13', 'F9': 'F8', 'F12': 'F1', 'F5': 'F7', 'F2': 'F9', 'F3': 'F2', 'F4': 'F11'}

{'C2': 'C2', 'C1': 'C1'}

High

{'C2': 'Low', 'C1': 'High'}

LogisticRegression

C3

Concrete Strength Classification

Probably C3 is the right label for this case since the probability of the alternative label, C2 and C1, are only 1.03% and 0.0%. The order of importance of the features for the above classification verdict is F3, F1, F5, F8, F4, F6, F2, and F7. Analysis conducted shows that only the features F1, F4, and F6 have negative contributions, hence reducing the probability of assigning label C3 to the given case. Positive features that increase the likelihood that C3 is the valid label are F3, F5, F8, F2, and F7. The co-attribution of the positive variables is stronger than that of the negative ones, so it is not surprising that we see the level of confidence associated with the prediction of class C3.

{'C2': '1.03%', 'C3': '98.97%', 'C1': '0.0%'}

{'F3': 'cement', 'F1': 'age_days', 'F5': 'water', 'F8': 'superplasticizer', 'F4': 'fineaggregate', 'F6': 'flyash', 'F2': 'slag', 'F7': 'coarseaggregate'}

{'F1': 'F3', 'F8': 'F1', 'F4': 'F5', 'F5': 'F8', 'F7': 'F4', 'F3': 'F6', 'F2': 'F2', 'F6': 'F7'}

{'C3': 'C2', 'C1': 'C3', 'C2': 'C1'}

Strong

{'C2': 'Weak', 'C3': 'Strong', 'C1': 'Other'}

DNN

C1

Credit Card Fraud Classification

The model labels the given data as C1 since it has a higher predicted probability equal to 51.42% compared to that of C2 which is equal to 48.58%. The input variables with higher contributions to the above classification decision are F27, F25, F5, F4, and F17, while those with little influence are F10, F14, F3, F21, and F1. Positively supporting the choice of the label, in this case, are mainly F27, F25, F17, and F4. However, the main negative variables are F5, F22, and F7. Judging based on the degree of influence as well as the direction of influence of the variables, it is not surprising that the model is only 51.42% confident in the assigned label which is marginally above average.

{'C2': '48.58%', 'C1': '51.42%'}

{'F25': 'Z18', 'F27': 'Z14', 'F5': 'Time', 'F4': 'Z1', 'F17': 'Z19', 'F12': 'Z10', 'F8': 'Z4', 'F24': 'Z3', 'F6': 'Z12', 'F16': 'Z16', 'F22': 'Z7', 'F7': 'Z11', 'F20': 'Z9', 'F18': 'Z6', 'F15': 'Z23', 'F29': 'Z5', 'F28': 'Z17', 'F13': 'Z21', 'F2': 'Z24', 'F19': 'Z8', 'F30': 'Amount', 'F26': 'Z20', 'F23': 'Z27', 'F9': 'Z25', 'F11': 'Z13', 'F10': 'Z2', 'F14': 'Z22', 'F3': 'Z28', 'F21': 'Z26', 'F1': 'Z15'}

{'F19': 'F25', 'F15': 'F27', 'F1': 'F5', 'F2': 'F4', 'F20': 'F17', 'F11': 'F12', 'F5': 'F8', 'F4': 'F24', 'F13': 'F6', 'F17': 'F16', 'F8': 'F22', 'F12': 'F7', 'F10': 'F20', 'F7': 'F18', 'F24': 'F15', 'F6': 'F29', 'F18': 'F28', 'F22': 'F13', 'F25': 'F2', 'F9': 'F19', 'F30': 'F30', 'F21': 'F26', 'F28': 'F23', 'F26': 'F9', 'F14': 'F11', 'F3': 'F10', 'F23': 'F14', 'F29': 'F3', 'F27': 'F21', 'F16': 'F1'}

{'C1': 'C2', 'C2': 'C1'}

Fraud

{'C2': 'Not Fraud', 'C1': 'Fraud'}

SGDClassifier

C2

Company Bankruptcy Prediction

The following is the classification for the provided data: C2 is the most likely class label and C1 cannot possibly be the correct label given the likelihood is 0.0%. F85, F60, and F74 are the key variables that contributed to the classification choice. However, the classifier does not consider all features while making this conclusion, and these irrelevant features include F53, F11, F24, and F9. Revealed to have positive contributions to the prediction made here among the top features are F74, F22, F28, and F82, but all of the others, F85, F60, F55, F30, F43, F21, and F47, argue against labelling the present scenario as C1 and despite the fact that the bulk of relevant features are pointing in the opposite direction, the classifier is extremely certain that the proper label for the current scenario is C2, not C1.

{'C1': '0.00%', 'C2': '100.00%'}

{'F85': ' Liability to Equity', 'F60': ' Net worth\\/Assets', 'F74': ' Debt ratio %', 'F55': " Net Income to Stockholder's Equity", 'F47': ' Equity to Liability', 'F82': ' Realized Sales Gross Margin', 'F30': ' Net Value Per Share (A)', 'F43': ' Current Liability to Assets', 'F22': ' Current Liability to Equity', 'F28': ' Net Income to Total Assets', 'F21': ' Operating Profit Per Share (Yuan ¥)', 'F52': ' ROA(B) before interest and depreciation after tax', 'F58': ' Working Capital to Total Assets', 'F36': ' Persistent EPS in the Last Four Seasons', 'F87': ' Current Liabilities\\/Equity', 'F83': ' Total expense\\/Assets', 'F5': ' Net Value Per Share (C)', 'F10': ' Gross Profit to Sales', 'F27': ' Pre-tax net Interest Rate', 'F62': ' Cash\\/Current Liability', 'F53': ' Total assets to GNP price', 'F11': ' Working capitcal Turnover Rate', 'F24': ' Net profit before tax\\/Paid-in capital', 'F9': ' Quick Assets\\/Current Liability', 'F79': ' Inventory and accounts receivable\\/Net value', 'F35': ' Long-term Liability to Current Assets', 'F84': ' Working Capital\\/Equity', 'F77': ' Operating Expense Rate', 'F33': ' Cash Reinvestment %', 'F20': ' Retained Earnings to Total Assets', 'F2': ' Cash Flow Per Share', 'F12': ' Contingent liabilities\\/Net worth', 'F69': ' Inventory\\/Working Capital', 'F57': ' Operating Gross Margin', 'F18': ' Current Asset Turnover Rate', 'F92': ' Interest Coverage Ratio (Interest expense to EBIT)', 'F16': ' Fixed Assets to Assets', 'F46': ' CFO to Assets', 'F31': ' Operating Profit Rate', 'F59': ' Total Asset Turnover', 'F93': ' Borrowing dependency', 'F7': ' Non-industry income and expenditure\\/revenue', 'F29': ' Current Liability to Liability', 'F78': ' Operating profit\\/Paid-in capital', 'F17': ' Revenue per person', 'F81': ' Operating Funds to Liability', 'F40': ' Current Liability to Current Assets', 'F51': ' Regular Net Profit Growth Rate', 'F66': ' Quick Ratio', 'F70': ' Total debt\\/Total net worth', 'F19': ' Current Ratio', 'F75': ' Tax rate (A)', 'F64': ' After-tax Net Profit Growth Rate', 'F49': ' After-tax net Interest Rate', 'F1': ' Per Share Net profit before tax (Yuan ¥)', 'F56': ' Continuous interest rate (after tax)', 'F45': ' No-credit Interval', 'F67': ' Total income\\/Total expense', 'F86': ' Allocation rate per person', 'F23': ' Total Asset Return Growth Rate Ratio', 'F41': ' Degree of Financial Leverage (DFL)', 'F26': ' Cash Turnover Rate', 'F39': ' Quick Asset Turnover Rate', 'F25': ' Revenue Per Share (Yuan ¥)', 'F44': ' Research and development expense rate', 'F88': ' ROA(C) before interest and depreciation before interest', 'F50': ' ROA(A) before interest and % after tax', 'F89': ' Net Value Per Share (B)', 'F65': ' Cash\\/Total Assets', 'F32': ' Cash Flow to Sales', 'F6': ' Total Asset Growth Rate', 'F61': ' Equity to Long-term Liability', 'F73': ' Fixed Assets Turnover Frequency', 'F3': ' Inventory\\/Current Liability', 'F8': ' Cash flow rate', 'F90': ' Realized Sales Gross Profit Growth Rate', 'F13': ' Inventory Turnover Rate (times)', 'F71': ' Cash Flow to Total Assets', 'F14': ' Net Worth Turnover Rate (times)', 'F37': ' Continuous Net Profit Growth Rate', 'F76': ' Cash Flow to Equity', 'F15': ' Long-term fund suitability ratio (A)', 'F68': ' Current Liabilities\\/Liability', 'F38': ' Cash Flow to Liability', 'F63': ' Accounts Receivable Turnover', 'F72': ' Current Assets\\/Total Assets', 'F4': ' Interest Expense Ratio', 'F91': ' Quick Assets\\/Total Assets', 'F54': ' Net Value Growth Rate', 'F34': ' Operating Profit Growth Rate', 'F42': ' Operating profit per person', 'F48': ' Average Collection Days', 'F80': ' Interest-bearing debt interest rate'}

{'F66': 'F85', 'F84': 'F60', 'F47': 'F74', 'F59': 'F55', 'F91': 'F47', 'F83': 'F82', 'F42': 'F30', 'F46': 'F43', 'F92': 'F22', 'F16': 'F28', 'F63': 'F21', 'F13': 'F52', 'F67': 'F58', 'F8': 'F36', 'F39': 'F87', 'F44': 'F83', 'F88': 'F5', 'F93': 'F10', 'F48': 'F27', 'F32': 'F62', 'F9': 'F53', 'F73': 'F11', 'F72': 'F24', 'F71': 'F9', 'F70': 'F79', 'F69': 'F35', 'F68': 'F84', 'F19': 'F77', 'F75': 'F33', 'F10': 'F20', 'F65': 'F2', 'F64': 'F12', 'F11': 'F69', 'F62': 'F57', 'F61': 'F18', 'F60': 'F92', 'F74': 'F16', 'F76': 'F46', 'F58': 'F31', 'F77': 'F59', 'F3': 'F93', 'F4': 'F7', 'F90': 'F29', 'F89': 'F78', 'F5': 'F17', 'F87': 'F81', 'F86': 'F40', 'F85': 'F51', 'F6': 'F66', 'F7': 'F70', 'F82': 'F19', 'F81': 'F75', 'F80': 'F64', 'F79': 'F49', 'F78': 'F1', 'F12': 'F56', 'F56': 'F45', 'F57': 'F67', 'F20': 'F86', 'F36': 'F23', 'F35': 'F41', 'F34': 'F26', 'F33': 'F39', 'F31': 'F25', 'F30': 'F44', 'F29': 'F88', 'F28': 'F50', 'F27': 'F89', 'F26': 'F65', 'F25': 'F32', 'F24': 'F6', 'F23': 'F61', 'F22': 'F73', 'F21': 'F3', 'F37': 'F8', 'F38': 'F90', 'F18': 'F13', 'F49': 'F71', 'F55': 'F14', 'F54': 'F37', 'F53': 'F76', 'F52': 'F15', 'F51': 'F68', 'F50': 'F38', 'F2': 'F63', 'F40': 'F72', 'F14': 'F4', 'F45': 'F91', 'F15': 'F54', 'F43': 'F34', 'F17': 'F42', 'F41': 'F48', 'F1': 'F80'}

{'C2': 'C1', 'C1': 'C2'}

Yes

{'C1': 'No', 'C2': 'Yes'}

GradientBoostingClassifier

C1

Food Ordering Customer Churn Prediction

The prediction probability of C2 is 17.93% and that of C1 is 82.07%. Therefore, the most probable class for the given case is C1. The above classification assertion statements are based on the information supplied to the classifier about the case given. The top features with significant attributions leading to the decision made above are F39, F26, F5, F19, F46, and F12. Conversely, F25, F11, F31, F1, and F14 are among the features deemed irrelevant to the classification decision here since their contributions are almost negligible and much closer to zero. The attribution analysis suggests that not all the relevant features positively contribute to the classifier's arriving at the verdict here. Those with positive attributions that push the classifier towards generating C1 as the label are F39, F26, F5, F19, F44, F35, F33, and F29. Decreasing the likelihood of the correctness of C1 are the negative features such as F46, F7, F12, F6, F17, F28, F16, and F37, which could be blamed for the little uncertainty in the classification output, as indicated by the prediction probability of C2.

{'C2': '17.93%', 'C1': '82.07%'}

{'F39': 'More restaurant choices', 'F26': 'Ease and convenient', 'F5': 'Bad past experience', 'F19': 'Time saving', 'F12': 'Unaffordable', 'F46': 'Educational Qualifications', 'F7': 'Late Delivery', 'F35': 'Occupation', 'F17': 'Influence of rating', 'F28': 'Less Delivery time', 'F6': 'Order placed by mistake', 'F16': 'Delivery person ability', 'F37': 'Order Time', 'F44': 'Unavailability', 'F9': 'More Offers and Discount', 'F45': 'Delay of delivery person picking up food', 'F33': 'Good Taste ', 'F29': 'Wrong order delivered', 'F30': 'Freshness ', 'F40': 'Missing item', 'F25': 'Residence in busy location', 'F31': 'Google Maps Accuracy', 'F11': 'Age', 'F14': 'Good Road Condition', 'F1': 'Low quantity low time', 'F32': 'High Quality of package', 'F22': 'Number of calls', 'F10': 'Politeness', 'F15': 'Temperature', 'F27': 'Maximum wait time', 'F41': 'Long delivery time', 'F43': 'Influence of time', 'F36': 'Delay of delivery person getting assigned', 'F23': 'Family size', 'F3': 'Poor Hygiene', 'F24': 'Health Concern', 'F2': 'Self Cooking', 'F42': 'Good Tracking system', 'F13': 'Good Food quality', 'F8': 'Easy Payment option', 'F38': 'Perference(P2)', 'F21': 'Perference(P1)', 'F20': 'Monthly Income', 'F34': 'Marital Status', 'F18': 'Gender', 'F4': 'Good Quantity'}

{'F12': 'F39', 'F10': 'F26', 'F21': 'F5', 'F11': 'F19', 'F23': 'F12', 'F6': 'F46', 'F19': 'F7', 'F4': 'F35', 'F38': 'F17', 'F39': 'F28', 'F29': 'F6', 'F37': 'F16', 'F31': 'F37', 'F22': 'F44', 'F14': 'F9', 'F26': 'F45', 'F45': 'F33', 'F27': 'F29', 'F43': 'F30', 'F28': 'F40', 'F33': 'F25', 'F34': 'F31', 'F1': 'F11', 'F35': 'F14', 'F36': 'F1', 'F40': 'F32', 'F41': 'F22', 'F42': 'F10', 'F44': 'F15', 'F32': 'F27', 'F24': 'F41', 'F30': 'F43', 'F25': 'F36', 'F7': 'F23', 'F20': 'F3', 'F18': 'F24', 'F17': 'F2', 'F16': 'F42', 'F15': 'F13', 'F13': 'F8', 'F9': 'F38', 'F8': 'F21', 'F5': 'F20', 'F3': 'F34', 'F2': 'F18', 'F46': 'F4'}

{'C1': 'C2', 'C2': 'C1'}

Go Away

{'C2': 'Return', 'C1': 'Go Away'}

DNN

C2

Credit Card Fraud Classification

The classification algorithm classifies the given case as C2 with a confidence level equal to 99.99%, suggesting that there is little chance that the C1 label could be the true label. The classification confidence level can be attributed to the influence and contributions of the features F28, F18, F29, F13, and F16. Positively supporting the model's decision are values of F28, F18, F29, and F13. On the contrary, the values of F16, F5, F14, and F23 are shifting the model towards producing the C1 label, which results in a marginal decrease in the certainty associated with the C2 label. The other positively supported features further improving the odds in favour of C2 include F25, F4, F22, and F2. Overall, it is not farfetched to accept that C2 is the correct label for the case under consideration since the strong positive influences of F28, F18, and F29 far outweigh the influence of any of the other input features. In other words, as mentioned above, there is only a small chance that the true label is not C2 considering the attributions of the top influential input features.

{'C2': '99.99%', 'C1': '0.01%'}

{'F28': 'Z3', 'F18': 'Z6', 'F29': 'Time', 'F16': 'Z13', 'F13': 'Z12', 'F5': 'Z4', 'F23': 'Z10', 'F25': 'Z5', 'F14': 'Z9', 'F21': 'Z14', 'F8': 'Z16', 'F30': 'Z11', 'F4': 'Z17', 'F26': 'Z19', 'F22': 'Z8', 'F3': 'Z28', 'F24': 'Z21', 'F19': 'Z20', 'F11': 'Z1', 'F7': 'Z24', 'F1': 'Z18', 'F2': 'Z2', 'F12': 'Z25', 'F27': 'Amount', 'F10': 'Z26', 'F15': 'Z27', 'F6': 'Z22', 'F17': 'Z15', 'F9': 'Z7', 'F20': 'Z23'}

{'F4': 'F28', 'F7': 'F18', 'F1': 'F29', 'F14': 'F16', 'F13': 'F13', 'F5': 'F5', 'F11': 'F23', 'F6': 'F25', 'F10': 'F14', 'F15': 'F21', 'F17': 'F8', 'F12': 'F30', 'F18': 'F4', 'F20': 'F26', 'F9': 'F22', 'F29': 'F3', 'F22': 'F24', 'F21': 'F19', 'F2': 'F11', 'F25': 'F7', 'F19': 'F1', 'F3': 'F2', 'F26': 'F12', 'F30': 'F27', 'F27': 'F10', 'F28': 'F15', 'F23': 'F6', 'F16': 'F17', 'F8': 'F9', 'F24': 'F20'}

{'C1': 'C2', 'C2': 'C1'}

Not Fraud

{'C2': 'Not Fraud', 'C1': 'Fraud'}

RandomForestClassifier

C1

Employee Attrition

The data is marked as C1 by the classifier based on the input features, with a moderate degree of confidence since the prediction probability of the other label, C2, is only 44.0%. The most influential features driving the classification above are F25, F1, F7, F29, F15, F12, F5, F30, F11, F24, F26, F19, F28, F27, F16, F10, F14, F21, and F9. Strongly reducing the chance of C1 being the true label for the given case are the negative features F1 and F25. Actually, these negative features, along with other features such as F15, F12, and F11, are responsible for the uncertainty in the classification decision here. On the contrary, the input features F7, F29, F5, F30, F24, and F26 positively contribute to the classifier's decision to choose C1 as the label here. Finally, it is important to note that not all the features are shown to be relevant when making the labelling decision regarding the case under consideration, and these irrelevant features include F8, F6, F3, and F17.

{'C2': '44.00%', 'C1': '56.00%'}

{'F25': 'OverTime', 'F1': 'BusinessTravel', 'F7': 'MaritalStatus', 'F29': 'JobInvolvement', 'F15': 'WorkLifeBalance', 'F12': 'Education', 'F5': 'EnvironmentSatisfaction', 'F30': 'Gender', 'F11': 'JobRole', 'F24': 'NumCompaniesWorked', 'F26': 'YearsInCurrentRole', 'F19': 'HourlyRate', 'F28': 'Department', 'F27': 'RelationshipSatisfaction', 'F16': 'PerformanceRating', 'F2': 'YearsWithCurrManager', 'F10': 'Age', 'F14': 'MonthlyRate', 'F21': 'StockOptionLevel', 'F9': 'JobSatisfaction', 'F8': 'DailyRate', 'F20': 'YearsSinceLastPromotion', 'F23': 'YearsAtCompany', 'F17': 'TrainingTimesLastYear', 'F13': 'EducationField', 'F3': 'TotalWorkingYears', 'F22': 'PercentSalaryHike', 'F4': 'MonthlyIncome', 'F18': 'JobLevel', 'F6': 'DistanceFromHome'}

{'F26': 'F25', 'F17': 'F1', 'F25': 'F7', 'F29': 'F29', 'F20': 'F15', 'F27': 'F12', 'F28': 'F5', 'F23': 'F30', 'F24': 'F11', 'F8': 'F24', 'F14': 'F26', 'F4': 'F19', 'F21': 'F28', 'F18': 'F27', 'F19': 'F16', 'F16': 'F2', 'F1': 'F10', 'F7': 'F14', 'F10': 'F21', 'F30': 'F9', 'F2': 'F8', 'F15': 'F20', 'F13': 'F23', 'F12': 'F17', 'F22': 'F13', 'F11': 'F3', 'F9': 'F22', 'F6': 'F4', 'F5': 'F18', 'F3': 'F6'}

{'C1': 'C2', 'C2': 'C1'}

Leave

{'C2': 'Leave', 'C1': 'Leave'}

RandomForestClassifier

C2

Cab Surge Pricing System

The model determined that this case belongs to C2 of the three possible labels, with an 83.0% likelihood. It is important to note, however, that there is about a 14.0% chance that it could be C1 and a 3.0% chance that it is rather C3. The most relevant feature driving this prediction is F1, with a very strong positive attribution, increasing the odds of the label C2. The following attributes have values pushing for a different prediction: F5, F3, F11, and F12, however, their attributions are very low when compared to that from F1. Other features positively contributing to the model's decision for this test case are F7, F4, F6, F2, F8, F9, and F10, with F8, F9, and F10 being the least relevant features considered by the model for the given case.

{'C3': '3.00%', 'C2': '83.00%', 'C1': '14.00%'}

{'F1': 'Type_of_Cab', 'F5': 'Destination_Type', 'F3': 'Trip_Distance', 'F11': 'Cancellation_Last_1Month', 'F7': 'Confidence_Life_Style_Index', 'F4': 'Var3', 'F12': 'Customer_Since_Months', 'F6': 'Life_Style_Index', 'F2': 'Var2', 'F8': 'Gender', 'F9': 'Var1', 'F10': 'Customer_Rating'}

{'F2': 'F1', 'F6': 'F5', 'F1': 'F3', 'F8': 'F11', 'F5': 'F7', 'F11': 'F4', 'F3': 'F12', 'F4': 'F6', 'F10': 'F2', 'F12': 'F8', 'F9': 'F9', 'F7': 'F10'}

{'C3': 'C3', 'C1': 'C2', 'C2': 'C1'}

C2

{'C3': 'Low', 'C2': 'Medium', 'C1': 'High'}

RandomForestClassifier

C1

Annual Income Earnings

The classifier assigned the label C1, given that there is merely a 2.18% chance that C2 is the correct label. Influencing this classification decision are mainly the values of the variables F3, F12, F10, and F5 which are also commonly referred to as positive variables since they increase the response in favour of the predicted label. Other variables supporting the prediction of C1 are F14, F6, F2, and F4. However, unlike F3, F12, F10, and F5, these variables have a moderate impact on the classifier. The variables that decrease the likelihood that C1 is the correct label are F13, F1, F7, and F11 since they have values that swing the classification verdict in the direction of C2.

{'C1': '97.82%', 'C2': '2.18%'}

{'F3': 'Capital Gain', 'F12': 'Marital Status', 'F10': 'Relationship', 'F5': 'Age', 'F14': 'Education-Num', 'F6': 'Hours per week', 'F13': 'Occupation', 'F2': 'Capital Loss', 'F1': 'Sex', 'F4': 'Education', 'F7': 'Race', 'F9': 'fnlwgt', 'F11': 'Country', 'F8': 'Workclass'}

{'F11': 'F3', 'F6': 'F12', 'F8': 'F10', 'F1': 'F5', 'F5': 'F14', 'F13': 'F6', 'F7': 'F13', 'F12': 'F2', 'F10': 'F1', 'F4': 'F4', 'F9': 'F7', 'F3': 'F9', 'F14': 'F11', 'F2': 'F8'}

{'C1': 'C1', 'C2': 'C2'}

Under 50K

{'C1': 'Under 50K', 'C2': 'Above 50K'}

SVMClassifier_poly

C1

Employee Attrition

Because the chance that C2 is the right label is around 42.17 percent, the example under review is labelled as C1 with a moderate degree of confidence. F12, F9, F23, F24, F8, and F11 have the most influence on the above forecast, whereas F20, F27, F13, F28, F18, F15, and F26 have small contributions. F16, F19, F4, F22, F7, F3, and F10 all have a relatively modest impact. However, the classifier does not take into account all of the attributes while making a judgement in a specific case and the attributes F6, F25, F29, and F30 are all irrelevant features. F12, F9, F23, F8, F27, F18, and F3 are the positive features pushing the prediction in support of the forecasted label. We can see from the attributions map that the bulk of the influential features exhibit negative attributions that reduce the likelihood that C1 is the correct label, justifying the uncertainty associated with the classifier's prediction choice.

{'C1': '57.83%', 'C2': '42.17%'}

{'F12': 'OverTime', 'F9': 'NumCompaniesWorked', 'F23': 'RelationshipSatisfaction', 'F24': 'MaritalStatus', 'F8': 'YearsSinceLastPromotion', 'F11': 'Department', 'F20': 'Age', 'F27': 'Education', 'F13': 'EducationField', 'F28': 'BusinessTravel', 'F18': 'JobLevel', 'F15': 'JobInvolvement', 'F26': 'WorkLifeBalance', 'F16': 'MonthlyRate', 'F19': 'YearsAtCompany', 'F4': 'Gender', 'F22': 'PerformanceRating', 'F7': 'JobRole', 'F3': 'TrainingTimesLastYear', 'F10': 'EnvironmentSatisfaction', 'F6': 'YearsWithCurrManager', 'F25': 'DailyRate', 'F29': 'YearsInCurrentRole', 'F30': 'TotalWorkingYears', 'F21': 'StockOptionLevel', 'F14': 'PercentSalaryHike', 'F1': 'MonthlyIncome', 'F2': 'HourlyRate', 'F17': 'DistanceFromHome', 'F5': 'JobSatisfaction'}

{'F26': 'F12', 'F8': 'F9', 'F18': 'F23', 'F25': 'F24', 'F15': 'F8', 'F21': 'F11', 'F1': 'F20', 'F27': 'F27', 'F22': 'F13', 'F17': 'F28', 'F5': 'F18', 'F29': 'F15', 'F20': 'F26', 'F7': 'F16', 'F13': 'F19', 'F23': 'F4', 'F19': 'F22', 'F24': 'F7', 'F12': 'F3', 'F28': 'F10', 'F16': 'F6', 'F2': 'F25', 'F14': 'F29', 'F11': 'F30', 'F10': 'F21', 'F9': 'F14', 'F6': 'F1', 'F4': 'F2', 'F3': 'F17', 'F30': 'F5'}

{'C1': 'C1', 'C2': 'C2'}

Leave

{'C1': 'Leave', 'C2': 'Leave'}

BernoulliNB

C1

Credit Card Fraud Classification

All features are shown to have a positive impact on the classification to class C1 or to have no impact at all. F14, F28, F30, and F1 are the four features with the most impact. Some of the remaining features, in order of feature importance, are F8, F16, F7, F24, F5, F13, F4, F21, F26, F18, F12, and F15. F14 and F28 both have the highest positive impact on the final classification, pushing the classification towards class C1. All of F30, F1, F8, and F16 influence the model's classification to C1. In terms of the features which have a positive impact on the classification, features F7, F24, F5, and F13 are all ranked to have a medium degree of influence on the final classification. F7 and F24 both have a similar importance attribution, which is higher than that of F5 and F13. All the other features not listed above are irrelevant to the decision above and among them are F20, F19, and F3.

{'C2': '5.16%', 'C1': '94.84%'}

{'F14': 'Z14', 'F28': 'Z1', 'F30': 'Z17', 'F1': 'Amount', 'F8': 'Z19', 'F16': 'Z5', 'F7': 'Z3', 'F24': 'Z8', 'F5': 'Z18', 'F13': 'Z10', 'F4': 'Z26', 'F21': 'Z25', 'F26': 'Z22', 'F18': 'Z4', 'F12': 'Z7', 'F15': 'Z13', 'F23': 'Z23', 'F6': 'Z9', 'F11': 'Z21', 'F29': 'Z2', 'F20': 'Z28', 'F3': 'Z24', 'F19': 'Z27', 'F25': 'Time', 'F9': 'Z20', 'F22': 'Z16', 'F17': 'Z12', 'F10': 'Z11', 'F2': 'Z6', 'F27': 'Z15'}

{'F15': 'F14', 'F2': 'F28', 'F18': 'F30', 'F30': 'F1', 'F20': 'F8', 'F6': 'F16', 'F4': 'F7', 'F9': 'F24', 'F19': 'F5', 'F11': 'F13', 'F27': 'F4', 'F26': 'F21', 'F23': 'F26', 'F5': 'F18', 'F8': 'F12', 'F14': 'F15', 'F24': 'F23', 'F10': 'F6', 'F22': 'F11', 'F3': 'F29', 'F29': 'F20', 'F25': 'F3', 'F28': 'F19', 'F1': 'F25', 'F21': 'F9', 'F17': 'F22', 'F13': 'F17', 'F12': 'F10', 'F7': 'F2', 'F16': 'F27'}

{'C2': 'C2', 'C1': 'C1'}

Fraud

{'C2': 'Not Fraud', 'C1': 'Fraud'}

LogisticRegression

C1

Used Cars Price-Range Prediction

Label C2 has a lower probability than label C1, so C1 is the most likely option in this case. C1 has a probability of approximately 96.25 percent, which can be attributed to variables such as F3, F6, F9, and F1. According to the attributions assessment, the least relevant variables are F10, F2, and F4. Inspection of the direction of influence of the features showed that F5 and F7 present negative contributions that push the model somewhat away from producing C1 because they support the label C2. Considering that the combined impact of the negative variables is quite minimal in comparison to the combined impact of the positive variables such as F3, F6, F9, F8, and F1, it is not surprising that the model is very certain that C2 is not the accurate label for the given case.

{'C1': '96.25%', 'C2': '3.75%'}

{'F6': 'Fuel_Type', 'F3': 'Power', 'F9': 'Engine', 'F1': 'Seats', 'F5': 'car_age', 'F7': 'Owner_Type', 'F8': 'Name', 'F10': 'Mileage', 'F2': 'Kilometers_Driven', 'F4': 'Transmission'}

{'F7': 'F6', 'F4': 'F3', 'F3': 'F9', 'F10': 'F1', 'F5': 'F5', 'F9': 'F7', 'F6': 'F8', 'F2': 'F10', 'F1': 'F2', 'F8': 'F4'}

{'C1': 'C1', 'C2': 'C2'}

Low

{'C1': 'Low', 'C2': 'High'}

GradientBoostingClassifier

C2

Paris House Classification

According to the prediction made here, the most likely label for the given case is C2, with a prediction probability of 97.02%, indicating that the prediction probability of C1 is only 2.98%. The classification above is mainly due to the influence of F10, F14, and F11. The next set of features with moderate contributions includes F6, F16, and F13. However, those with little consideration from the classifier are F12, F7, F8, and F1. In consideration of the fact that all the top four features have a strong positive contribution, it is foreseeable why the classifier is relatively confident that the correct label for this case is C2. Additionally, the negative attributes with moderate to low impact are F16, F17, and F3.

{'C1': '2.98%', 'C2': '97.02%'}

{'F10': 'isNewBuilt', 'F14': 'hasYard', 'F11': 'hasPool', 'F6': 'hasStormProtector', 'F16': 'made', 'F13': 'squareMeters', 'F17': 'floors', 'F3': 'cityCode', 'F4': 'hasGuestRoom', 'F2': 'basement', 'F5': 'numPrevOwners', 'F9': 'price', 'F15': 'numberOfRooms', 'F12': 'garage', 'F7': 'cityPartRange', 'F8': 'hasStorageRoom', 'F1': 'attic'}

{'F3': 'F10', 'F1': 'F14', 'F2': 'F11', 'F4': 'F6', 'F12': 'F16', 'F6': 'F13', 'F8': 'F17', 'F9': 'F3', 'F16': 'F4', 'F13': 'F2', 'F11': 'F5', 'F17': 'F9', 'F7': 'F15', 'F15': 'F12', 'F10': 'F7', 'F5': 'F8', 'F14': 'F1'}

{'C1': 'C1', 'C2': 'C2'}

Luxury

{'C1': 'Basic', 'C2': 'Luxury'}

RandomForestClassifier

C2

Flight Price-Range Classification

The classification conclusion is as follows: C2 is the most likely label for this case and the classifier is certain that neither C3 nor C1 are the right labels since their likelihoods are equal to zero. The driving factors for the above classification are F4, F12, and F3, all of which have a substantial positive impact, causing the classifier to select C2. F5, F2, F1, and F8 are also positive features. The assigned label is not supported by all of the input features since the negative features F6, F9, and F7 support the decision that the most likely class for this instance could be any one of the other labels, C1 and C3. Nevertheless, given the confidence level in the aforementioned classification, it is reasonable to assume that the classifier paid little attention to the negative features, resulting in the selection of class C2.

{'C2': '100.00%', 'C3': '0.00%', 'C1': '0.00%'}

{'F4': 'Airline', 'F12': 'Duration_hours', 'F3': 'Total_Stops', 'F5': 'Journey_month', 'F6': 'Source', 'F2': 'Destination', 'F1': 'Arrival_hour', 'F9': 'Journey_day', 'F7': 'Dep_minute', 'F8': 'Arrival_minute', 'F10': 'Duration_mins', 'F11': 'Dep_hour'}

{'F9': 'F4', 'F7': 'F12', 'F12': 'F3', 'F2': 'F5', 'F10': 'F6', 'F11': 'F2', 'F5': 'F1', 'F1': 'F9', 'F4': 'F7', 'F6': 'F8', 'F8': 'F10', 'F3': 'F11'}

{'C1': 'C2', 'C3': 'C3', 'C2': 'C1'}

Low

{'C2': 'Low', 'C3': 'Moderate', 'C1': 'High'}

KNeighborsClassifier

C1

Water Quality Classification

The classifier states that there is a 50.0% chance that the true label of this test observation is C1. This indicates that the classifier is less certain in its prediction decision regarding the case under consideration. The label assigned is mainly due to the values of the features F2, F4, F7, F6, F5, and F3. The top features, F2 and F4, have very strong positive contributions pushing the prediction higher towards the most probable label. Among the remaining features stated above, F7, F6, F5, and F3, only F3 demonstrates some level of contradiction, forcing the labelling decision in a different direction. Finally, the features with marginal impact on the prediction made here are F8, F1, and F9. While F8 and F9 positively influence the decision made, F1 suggests that the label assigned by the classifier might not be the true label.

{'C1': '50.00%', 'C2': '50.00%'}

{'F2': 'Hardness', 'F4': 'Sulfate', 'F7': 'Organic_carbon', 'F6': 'Solids', 'F5': 'Conductivity', 'F3': 'Trihalomethanes', 'F8': 'ph', 'F1': 'Turbidity', 'F9': 'Chloramines'}

{'F2': 'F2', 'F5': 'F4', 'F7': 'F7', 'F3': 'F6', 'F6': 'F5', 'F8': 'F3', 'F1': 'F8', 'F9': 'F1', 'F4': 'F9'}

{'C2': 'C1', 'C1': 'C2'}

Not Portable

{'C1': 'Not Portable', 'C2': 'Portable'}

GradientBoostingClassifier

C1

Printer Sales

According to the attribution analysis, the each input variables contributes differently to the decision. For the case under consideration, there are variables that have negative influence on the decision here, but it also has numerous quantifiable variables that are positive. Per the model, C1 is 91.95% certain to be the correct label and C2 has a predicted probability of only 8.05%. The most essential input variables are F24, F18, F7, and F19, which allow the model to effectively compute the likelihoods across the classes, C1 and C2. F21 and F13 have nearly comparable positive effects, but F22 and F20 have a negative influence, altering the output decision in favour of a different label. The cumulative positive contribution of F21, F24, F19, F9, F17, and F13 was greater than that of F18, F7, F20, and F22, hence the positive variables succeed at improving the predictability odds in favour of the C1 class. Furthermore per the variable attributions, the contributions of F5, F2, F3, and F11 has very little to do with the classification decision since their attributions are negligible and closer to zero than all the above-mentioned variables.

{'C2': '8.05%', 'C1': '91.95%'}

{'F24': 'X24', 'F18': 'X8', 'F19': 'X1', 'F7': 'X21', 'F20': 'X4', 'F13': 'X6', 'F21': 'X3', 'F22': 'X22', 'F9': 'X7', 'F17': 'X15', 'F8': 'X20', 'F16': 'X11', 'F23': 'X10', 'F26': 'X19', 'F14': 'X5', 'F25': 'X16', 'F6': 'X23', 'F4': 'X9', 'F10': 'X17', 'F15': 'X18', 'F1': 'X25', 'F12': 'X14', 'F11': 'X2', 'F3': 'X13', 'F2': 'X12', 'F5': 'X26'}

{'F24': 'F24', 'F8': 'F18', 'F1': 'F19', 'F21': 'F7', 'F4': 'F20', 'F6': 'F13', 'F3': 'F21', 'F22': 'F22', 'F7': 'F9', 'F15': 'F17', 'F20': 'F8', 'F11': 'F16', 'F10': 'F23', 'F19': 'F26', 'F5': 'F14', 'F16': 'F25', 'F23': 'F6', 'F9': 'F4', 'F17': 'F10', 'F18': 'F15', 'F25': 'F1', 'F14': 'F12', 'F2': 'F11', 'F13': 'F3', 'F12': 'F2', 'F26': 'F5'}

{'C1': 'C2', 'C2': 'C1'}

More

{'C2': 'Less', 'C1': 'More'}

LogisticRegression

C2

Annual Income Earnings

Deciding the most probable label for the given case on the basis of the values of the input variables, the classification algorithm's output decision is that: the probability of C2 being the correct label is 79.78%, the probability of C1 is 20.22%. Therefore, the most likely label is identified as C2 and the attribution analysis shows that all the variables contributed to some extent to the final decision by the algorithm with respect to the given case. The most influential variables are F8, F1, F9, and F6, but F3, F11, and F5 are the least influential ones. The analysis also indicates that F8, F6, F3, and F5 are responsible for the marginal doubt in the classification decision here hence they are commonly referred to as negative variables since their contributions only tend to shift the verdict in a different direction than the assigned label. Finally, the variables such as F1, F9, F4, F10, F13, and F2 are the positive variables that increase the algorithm's response in favour of outputting the C2 label.

{'C1': '20.22%', 'C2': '79.78%'}

{'F8': 'Capital Gain', 'F1': 'Marital Status', 'F9': 'Education', 'F6': 'Capital Loss', 'F4': 'Hours per week', 'F10': 'Sex', 'F13': 'Country', 'F2': 'Education-Num', 'F7': 'Occupation', 'F12': 'Race', 'F14': 'Age', 'F3': 'Workclass', 'F11': 'fnlwgt', 'F5': 'Relationship'}

{'F11': 'F8', 'F6': 'F1', 'F4': 'F9', 'F12': 'F6', 'F13': 'F4', 'F10': 'F10', 'F14': 'F13', 'F5': 'F2', 'F7': 'F7', 'F9': 'F12', 'F1': 'F14', 'F2': 'F3', 'F3': 'F11', 'F8': 'F5'}

{'C1': 'C1', 'C2': 'C2'}

Above 50K

{'C1': 'Under 50K', 'C2': 'Above 50K'}

LogisticRegression

C1

E-Commerce Shipping

The reliability of the classification verdict for this case is 71.57%, implying there is a 28.43% chance that the correct label could be C2. F4 has a significant negative impact on classification output since its contribution contradicts the labelling of the case as C1, hence favours labelling the case as C2. The values F8, F1, F5, F2, F9, F3, and F10 have a positive effect on the results, but still contributes less than the effect of F4. The analysis shows that F4 has an overwhelming negative impact or influence on the predictive decisions of the model here. F1, F5, F2, and F9 have a positive effect on model predictions. Due to the power of the F4 function, all other functions have little effect on the results. In summary, the uncertainty of the predictions can be explained by the control on the model by feature F4, which drags the classification decision favourably towards C2.

{'C1': '71.57%', 'C2': '28.43%'}

{'F4': 'Discount_offered', 'F8': 'Weight_in_gms', 'F1': 'Prior_purchases', 'F5': 'Product_importance', 'F2': 'Cost_of_the_Product', 'F9': 'Gender', 'F3': 'Customer_rating', 'F10': 'Warehouse_block', 'F7': 'Customer_care_calls', 'F6': 'Mode_of_Shipment'}

{'F2': 'F4', 'F3': 'F8', 'F8': 'F1', 'F9': 'F5', 'F1': 'F2', 'F10': 'F9', 'F7': 'F3', 'F4': 'F10', 'F6': 'F7', 'F5': 'F6'}

{'C2': 'C1', 'C1': 'C2'}

On-time

{'C1': 'On-time', 'C2': 'Late'}

DecisionTreeClassifier

C2

Vehicle Insurance Claims

This model predicted class label C2 with about 93.32% certainty, while there was about a 6.68% chance of the correct class being identified as a different label. Seven features, F8, F27, F21, F9, F12, F20, and F32, have higher impacts on the model prediction decision above. But the feature F8 has the largest positive impact on the result and on the contrary, F21, F12, F20, and F32 show the potential to shift the narrative to a different label since their contributions reduce the likelihood of the predicted label for this case. In addition, features F29, F1, and F13 have moderate impacts on the model's prediction but each of them is increasing the responses, and finally, the features shown have negligible influence include F26, and F11.

{'C2': '93.32%', 'C1': '6.68%'}

{'F8': 'incident_severity', 'F27': 'incident_city', 'F21': 'injury_claim', 'F9': 'insured_occupation', 'F12': 'insured_zip', 'F20': 'authorities_contacted', 'F32': 'auto_year', 'F29': 'police_report_available', 'F1': 'bodily_injuries', 'F13': 'insured_hobbies', 'F6': 'insured_sex', 'F4': 'auto_make', 'F30': 'property_damage', 'F25': 'witnesses', 'F33': 'insured_relationship', 'F31': 'age', 'F28': 'vehicle_claim', 'F19': 'months_as_customer', 'F22': 'property_claim', 'F23': 'incident_type', 'F17': 'capital-gains', 'F24': 'policy_deductable', 'F18': 'policy_annual_premium', 'F15': 'incident_state', 'F3': 'umbrella_limit', 'F2': 'total_claim_amount', 'F5': 'collision_type', 'F16': 'incident_hour_of_the_day', 'F7': 'insured_education_level', 'F26': 'number_of_vehicles_involved', 'F11': 'policy_csl', 'F14': 'policy_state', 'F10': 'capital-loss'}

{'F27': 'F8', 'F30': 'F27', 'F14': 'F21', 'F22': 'F9', 'F6': 'F12', 'F28': 'F20', 'F17': 'F32', 'F32': 'F29', 'F11': 'F1', 'F23': 'F13', 'F20': 'F6', 'F33': 'F4', 'F31': 'F30', 'F12': 'F25', 'F24': 'F33', 'F2': 'F31', 'F16': 'F28', 'F1': 'F19', 'F15': 'F22', 'F25': 'F23', 'F7': 'F17', 'F3': 'F24', 'F4': 'F18', 'F29': 'F15', 'F5': 'F3', 'F13': 'F2', 'F26': 'F5', 'F9': 'F16', 'F21': 'F7', 'F10': 'F26', 'F19': 'F11', 'F18': 'F14', 'F8': 'F10'}

{'C1': 'C2', 'C2': 'C1'}

Not Fraud

{'C2': 'Not Fraud', 'C1': 'Fraud'}

KNeighborsClassifier

C1

Printer Sales

Considering the prediction likelihoods, this case is labelled as C1 by the model, that is, the model states that there is about an 83.33% chance that the case is under C1 and about a 16.67% chance that it is not. The most relevant features influencing the decision made here are: F17, F23, F13, and F3. Among the feature set mentioned above, F17 and F23 offer a very strong positive contribution to the prediction of C1. Conversely, F3 suggests the alternative label C2 could be the true label for this case, but this attribution is weak when compared to F23 and F17. Other features that are moderately pushing for this classification decision include F9, F20, F12, and F10. However, the values of F5, F6, F16, and F25 advocate for the assignment of a different label. Finally, the features F18, F24, F11, and F14 have very little impact on the model's prediction for this case.

{'C1': '83.33%', 'C2': '16.67%'}

{'F17': 'X24', 'F23': 'X1', 'F3': 'X4', 'F13': 'X10', 'F9': 'X2', 'F20': 'X8', 'F5': 'X17', 'F12': 'X7', 'F10': 'X21', 'F6': 'X18', 'F16': 'X6', 'F25': 'X11', 'F21': 'X22', 'F2': 'X25', 'F8': 'X5', 'F22': 'X19', 'F19': 'X15', 'F4': 'X23', 'F26': 'X16', 'F7': 'X3', 'F18': 'X14', 'F24': 'X20', 'F11': 'X13', 'F14': 'X12', 'F15': 'X9', 'F1': 'X26'}

{'F24': 'F17', 'F1': 'F23', 'F4': 'F3', 'F10': 'F13', 'F2': 'F9', 'F8': 'F20', 'F17': 'F5', 'F7': 'F12', 'F21': 'F10', 'F18': 'F6', 'F6': 'F16', 'F11': 'F25', 'F22': 'F21', 'F25': 'F2', 'F5': 'F8', 'F19': 'F22', 'F15': 'F19', 'F23': 'F4', 'F16': 'F26', 'F3': 'F7', 'F14': 'F18', 'F20': 'F24', 'F13': 'F11', 'F12': 'F14', 'F9': 'F15', 'F26': 'F1'}

{'C2': 'C1', 'C1': 'C2'}

Less

{'C1': 'Less', 'C2': 'More'}

SVC

C2

Advertisement Prediction

For the given instance, the model generated the label C2 with a very high predicted probability equal to 99.66% which implies that the model is very confident that C1 is not the correct label. Ranking the contributions of the features to the prediction above, from the most relevant to the least relevant, is as follows: F4, F7, F1, F2, F3, F6, and F5. Among the seven features, only F3 and F6 have negative contributions, pushing the prediction towards the C1 label. However, given that these features have very low contributions, their impact on the model's decision is close to negligible when compared to the contributions of the positive features F4, F7, and F1.

{'C1': '0.34%', 'C2': '99.66%'}

{'F4': 'Daily Time Spent on Site', 'F7': 'Daily Internet Usage', 'F1': 'Age', 'F2': 'Gender', 'F3': 'ad_day', 'F6': 'ad_month', 'F5': 'Area Income'}

{'F1': 'F4', 'F4': 'F7', 'F2': 'F1', 'F5': 'F2', 'F7': 'F3', 'F6': 'F6', 'F3': 'F5'}

{'C2': 'C1', 'C1': 'C2'}

Watch

{'C1': 'Skip', 'C2': 'Watch'}

LogisticRegression

C2

Student Job Placement

The final prediction given by the model was C2 with almost 100% certainty, showing the model is confident about its decision. F3 had significantly more influence on the prediction than any other feature with F6 and F4 having the next highest attribution values. All the top features, F3, F6, and F4, encouraged the model to output class C2. F1, F10, and F7 are the features that had the least positive impact on the final classification. The features F12, F8, F11, and F9 have moderate impacts, pushing the model slightly away from a C2 classification.

{'C2': '98.47%', 'C1': '1.53%'}

{'F3': 'ssc_p', 'F6': 'hsc_p', 'F4': 'degree_p', 'F5': 'gender', 'F12': 'degree_t', 'F8': 'workex', 'F2': 'specialisation', 'F11': 'etest_p', 'F1': 'hsc_s', 'F9': 'hsc_b', 'F10': 'ssc_b', 'F7': 'mba_p'}

{'F1': 'F3', 'F2': 'F6', 'F3': 'F4', 'F6': 'F5', 'F10': 'F12', 'F11': 'F8', 'F12': 'F2', 'F4': 'F11', 'F9': 'F1', 'F8': 'F9', 'F7': 'F10', 'F5': 'F7'}

{'C1': 'C2', 'C2': 'C1'}

Not Placed

{'C2': 'Not Placed', 'C1': 'Placed'}

GradientBoostingClassifier

C1

Health Care Services Satisfaction Prediction

Based on the information provided to the classifier, the true label for the given case is likely C1, with a confidence level of 76.26%. Each input variable has a different degree of influence on the classifier's final labelling decision with respect to the case under consideration. Whilst F6, F15, and F13 have lower contributions to the classifier's decision, F5, F11, and F8 are identified as the major contributors resulting in the assignment and classification probabilities across the two classes. There is a 23.74% chance that perhaps C2 is the true label and the features responsible for this are the negative features, F8, F9, F3, F12, F16, F6, and F15. Driving the classifier's decision in favour of C1 are the positive features such as F5, F11, F4, F7, F1, F10, and F14.

{'C2': '23.74%', 'C1': '76.26%'}

{'F5': 'Exact diagnosis', 'F11': 'avaliablity of drugs', 'F8': 'lab services', 'F4': 'friendly health care workers', 'F7': 'Communication with dr', 'F9': 'Time waiting', 'F1': 'Specialists avaliable', 'F10': 'Modern equipment', 'F14': 'waiting rooms', 'F3': 'Check up appointment', 'F2': 'Hygiene and cleaning', 'F12': 'Admin procedures', 'F16': 'Time of appointment', 'F6': 'hospital rooms quality', 'F15': 'parking, playing rooms, caffes', 'F13': 'Quality\\/experience dr.'}

{'F9': 'F5', 'F13': 'F11', 'F12': 'F8', 'F11': 'F4', 'F8': 'F7', 'F2': 'F9', 'F7': 'F1', 'F10': 'F10', 'F14': 'F14', 'F1': 'F3', 'F4': 'F2', 'F3': 'F12', 'F5': 'F16', 'F15': 'F6', 'F16': 'F15', 'F6': 'F13'}

{'C2': 'C2', 'C1': 'C1'}

Satisfied

{'C2': 'Dissatisfied', 'C1': 'Satisfied'}

SGDClassifier

C3

Flight Price-Range Classification

According to the model, C1 is the least probable class, while the most probable class for the given case is identified as C3. The top two variables with the greatest control over the model in terms of this case's label assignment are F5 and F8 but on the contrary, the rest of the variables have moderate-to-lower influence. The contribution of F8 is negative, reducing the chances of selecting the label C3. F5, F6, and F12 drive the model to classify the given case as C3. Furthermore, both F10 and F9 have values that increase the predicted probability of C3, but F7 and F4 decrease the model's response in favour of any of the remaining classes. When choosing a label in this instance, the model pays little attention to the respective values of F1, F3, F2, and F11 hence they are the least ranked features.

{'C3': '86.54%', 'C2': '13.46%', 'C1': '0.00%'}

{'F5': 'Airline', 'F8': 'Total_Stops', 'F6': 'Source', 'F12': 'Journey_month', 'F7': 'Arrival_minute', 'F10': 'Journey_day', 'F9': 'Duration_hours', 'F4': 'Dep_hour', 'F1': 'Destination', 'F3': 'Arrival_hour', 'F2': 'Dep_minute', 'F11': 'Duration_mins'}

{'F9': 'F5', 'F12': 'F8', 'F10': 'F6', 'F2': 'F12', 'F6': 'F7', 'F1': 'F10', 'F7': 'F9', 'F3': 'F4', 'F11': 'F1', 'F5': 'F3', 'F4': 'F2', 'F8': 'F11'}

{'C2': 'C3', 'C1': 'C2', 'C3': 'C1'}

Low

{'C3': 'Low', 'C2': 'Moderate', 'C1': 'High'}

SVC

C1

Australian Credit Approval

Judging by the prediction probabilities, the most probable or likely class assigned by the classifier is C1, with the associated confidence level of 90.97%. The features with the most influence on the prediction above include F2, F11, and F13, while the least important features are F4, F9, and F14. Beside some of the features are shown to negatively contribute to the prediction made here and these negative features, F6, F11, F12, F3, and F4, reduce the classifier's response to generating label C1, consequently pushing the verdict towards C2. The joint impact of the negatives is smaller compared to that of positive features such as F2, F13, F1, and F5, hence the greater drive on the classifier to assign C1 as the correct label.

{'C2': '9.03%', 'C1': '90.97%'}

{'F2': 'A8', 'F11': 'A9', 'F13': 'A12', 'F6': 'A10', 'F1': 'A4', 'F12': 'A14', 'F3': 'A11', 'F5': 'A13', 'F8': 'A1', 'F7': 'A6', 'F10': 'A3', 'F4': 'A5', 'F9': 'A2', 'F14': 'A7'}

{'F8': 'F2', 'F9': 'F11', 'F12': 'F13', 'F10': 'F6', 'F4': 'F1', 'F14': 'F12', 'F11': 'F3', 'F13': 'F5', 'F1': 'F8', 'F6': 'F7', 'F3': 'F10', 'F5': 'F4', 'F2': 'F9', 'F7': 'F14'}

{'C2': 'C2', 'C1': 'C1'}

Class 2

{'C2': 'Class 1', 'C1': 'Class 2'}

KNeighborsClassifier

C2

Credit Risk Classification

According to the model employed, the label for the case is more likely to be C2. This assessment decision is mainly based on the inpacts of features such as F7, F6, F3, F2, and F11. Among these top features, F7, F6, and F3 have positive contributions to the prediction above, while F11 and F2 are identified as negative features which decreases the likelihood associated with class C2 for this case. Furthermore, the values of F1, F4, F5, and F10 also indicate that the other label, C1, may be the correct label but luckily, the influence of the above-mentioned negative features can be classified as only moderate when compared to F7, F6, and F3. In conclusion, with such a strong positive influence from F7, F6, F3, and F9, it is safe to say that the model is very accurate in its classification judgments, with 100.0% certainty.

{'C2': '100.00%', 'C1': '0.00%'}

{'F7': 'fea_4', 'F6': 'fea_8', 'F3': 'fea_2', 'F11': 'fea_9', 'F2': 'fea_6', 'F5': 'fea_10', 'F10': 'fea_1', 'F9': 'fea_7', 'F4': 'fea_11', 'F1': 'fea_3', 'F8': 'fea_5'}

{'F4': 'F7', 'F8': 'F6', 'F2': 'F3', 'F9': 'F11', 'F6': 'F2', 'F10': 'F5', 'F1': 'F10', 'F7': 'F9', 'F11': 'F4', 'F3': 'F1', 'F5': 'F8'}

{'C1': 'C2', 'C2': 'C1'}

Low

{'C2': 'Low', 'C1': 'High'}

BernoulliNB

C1

Personal Loan Modelling

From the prediction likelihood of each class label, the most probable label for the given case based on the values of its features is C1. The likelihood of C2 is negligible, hence we can conclude that the classifier is very confident that C1 is the correct label. Analysing the attributions of the input features showed that the most relevant feature with a strong influence on the classifier's decision here is F5. However, the classifier likely disregards the values of the irrelevant features, F6 and F1, when arriving at the classification above. The confidence level of the classifier employed to make the classification decision above is higher, mainly because the majority of the influential features have positive contributions. Positive features such as F5, F3, and F7 increase the classifier's response higher in favour of C1. F4 and F2 are the main negative features, but compared to F5, their influence on the above classification is very small.

{'C1': '99.99%', 'C2': '0.01%'}

{'F5': 'CD Account', 'F4': 'Income', 'F7': 'CCAvg', 'F3': 'Securities Account', 'F2': 'Education', 'F8': 'Family', 'F9': 'Mortgage', 'F6': 'Age', 'F1': 'Extra_service'}

{'F8': 'F5', 'F2': 'F4', 'F4': 'F7', 'F7': 'F3', 'F5': 'F2', 'F3': 'F8', 'F6': 'F9', 'F1': 'F6', 'F9': 'F1'}

{'C2': 'C1', 'C1': 'C2'}

Reject

{'C1': 'Reject', 'C2': 'Accept'}

LogisticRegression

C1

Bike Sharing Demand

The correct label for the given data instance, according to the machine learning algorithm, is C1 and this is mainly because the probability that C2 is the right label is only about 3.08%. From the analysis, the ranking of the input features based on their respective degree of influence is F10, F6, F7, F9, F12, F11, F4, F2, F8, F3, F5, and F1. This implies the most relevant features are F10, and F6 whereas F5 and F1 are the least relevant ones. Given that F8, F3, and F5 are the features that have a negative impact on the algorithm's selection in this case, it's no wonder that it's quite confident in the chosen class. The arguement towards labelling the case as C1 is also supported by the fact that the joint negative contributions of F8, F3, and F5 is very small when compared to that of the top positive features F10, F6, F7, F9, and F12.

{'C2': '3.08%', 'C1': '96.92%'}

{'F10': 'Functioning Day', 'F6': 'Rainfall(mm)', 'F7': 'Snowfall (cm)', 'F9': 'Solar Radiation (MJ\\/m2)', 'F12': 'Temperature', 'F11': 'Holiday', 'F4': 'Humidity(%)', 'F2': 'Seasons', 'F8': 'Hour', 'F3': 'Visibility (10m)', 'F5': 'Dew point temperature', 'F1': 'Wind speed (m\\/s)'}

{'F12': 'F10', 'F8': 'F6', 'F9': 'F7', 'F7': 'F9', 'F2': 'F12', 'F11': 'F11', 'F3': 'F4', 'F10': 'F2', 'F1': 'F8', 'F5': 'F3', 'F6': 'F5', 'F4': 'F1'}

{'C1': 'C2', 'C2': 'C1'}

More than 500

{'C2': 'Less than 500', 'C1': 'More than 500'}

MLPClassifier

C1

Hotel Satisfaction

Based on the values of the input variables, the prediction model labels the case given as C1 with very high certainty. Specifically, there is only about a 5.59% possibility that C2 is the correct label according to the model. The most influential factors leading to the above prediction decision are the values of F14, F4, and F3 whereas F6, F12, and F9 are deemed less relevant by the model. In between the two ends (most influential and least influential) are the features such as F11, F10, and F5 with moderate contributions. According to the attribution investigation performed, F14, F10, F5, F13, F8, and F9 have positive contributions, increasing the model's response to favour labelling the case as "C1". Conversely, features such as F4, F3, F15, and F11 provide negative contributions, resulting in a small shift toward selecting C2 as the correct class. In conclusion, given that the prediction likelihood of C2 is only 5.59%, it is obvious that the positive features outweigh the negative ones in terms of the considerations they receive from the model, hence the model's decision to assign the C1 label.

{'C1': '94.41%', 'C2': '5.59%'}

{'F14': 'Hotel wifi service', 'F4': 'Type of Travel', 'F3': 'Other service', 'F11': 'Stay comfort', 'F10': 'Type Of Booking', 'F5': 'Ease of Online booking', 'F15': 'Checkin\\/Checkout service', 'F13': 'Age', 'F8': 'Cleanliness', 'F1': 'Food and drink', 'F2': 'Hotel location', 'F7': 'Departure\\/Arrival convenience', 'F6': 'Gender', 'F12': 'purpose_of_travel', 'F9': 'Common Room entertainment'}

{'F6': 'F14', 'F3': 'F4', 'F14': 'F3', 'F11': 'F11', 'F4': 'F10', 'F8': 'F5', 'F13': 'F15', 'F5': 'F13', 'F15': 'F8', 'F10': 'F1', 'F9': 'F2', 'F7': 'F7', 'F1': 'F6', 'F2': 'F12', 'F12': 'F9'}

{'C2': 'C1', 'C1': 'C2'}

dissatisfied

{'C1': 'dissatisfied', 'C2': 'satisfied'}

LogisticRegression

C2

Airline Passenger Satisfaction

The data under consideration is labelled as C2 since it is the most probable label, with a prediction likelihood equal to 99.97% therefore classifier employed here is very confident that C1 is not the right label. The top features with the greatest influence on the classifier in terms of the above classification are F13, F8, F15, and F21. Conversely, the values of F18 and F4 have inconsiderable or insignificant influence on the decision made by the classifier. The input features with moderate to low influence but higher than F18 and F4 on the classifier include F1, F2, F22, and F17. The analysis also shows that the majority of the input features have positive attributions, explaining the level of confidence of the classifier as demonstrated by the prediction probabilities across the classes. The positive features increasing the odds of being labelled C2 include F13, F8, F15, F1, F2, F22, and F14. The marginal doubt in the prediction made here could be attributed to the influence of negative features such as F21, F17, F7, and F6. The negative features support classifying the given data as C1, but since their collective influence is smaller compared to that of the positives, the classifier is shifted more towards labelling the data as C2.

{'C1': '0.03%', 'C2': '99.97%'}

{'F8': 'Inflight wifi service', 'F13': 'Type of Travel', 'F15': 'Customer Type', 'F21': 'Online boarding', 'F1': 'Inflight service', 'F2': 'Baggage handling', 'F22': 'On-board service', 'F17': 'Departure\\/Arrival time convenient', 'F14': 'Seat comfort', 'F7': 'Inflight entertainment', 'F6': 'Gate location', 'F20': 'Cleanliness', 'F9': 'Ease of Online booking', 'F5': 'Class', 'F12': 'Leg room service', 'F16': 'Age', 'F19': 'Departure Delay in Minutes', 'F3': 'Arrival Delay in Minutes', 'F11': 'Gender', 'F10': 'Checkin service', 'F18': 'Food and drink', 'F4': 'Flight Distance'}

{'F7': 'F8', 'F4': 'F13', 'F2': 'F15', 'F12': 'F21', 'F19': 'F1', 'F17': 'F2', 'F15': 'F22', 'F8': 'F17', 'F13': 'F14', 'F14': 'F7', 'F10': 'F6', 'F20': 'F20', 'F9': 'F9', 'F5': 'F5', 'F16': 'F12', 'F3': 'F16', 'F21': 'F19', 'F22': 'F3', 'F1': 'F11', 'F18': 'F10', 'F11': 'F18', 'F6': 'F4'}

{'C2': 'C1', 'C1': 'C2'}

satisfied

{'C1': 'neutral or dissatisfied', 'C2': 'satisfied'}

BernoulliNB

C1

Used Cars Price-Range Prediction

C1 was the predicted category for the given case and the classifier is shown to be very certain about the above prediction verdict, given that the probability of C1 being the label is about 99.72%. The following five features all contributed positively towards the prediction of the C1 class with increasing levels of impact: F1, F2, F9, F5, and F4. F8 and F3 both had similar levels of impact on the prediction of C1, with F8 having a marginally stronger impact. F8 contributed towards the prediction of C1, while F3 contributed against it, in favour of an alternative label. F7 and F6 are the least relevant features, with very little impact, both with negative attributions, driving the prediction decision or verdict away from C1. From the analysis, only the features, F7, F6, and F3, are shown to have negative attributions, shifting the prediction away from C1. However, the collective attribution of F7, F6, and F3 is very low when compared to that of the positive features, so the classifier is motivated strongly by the positive features, leading to the prediction decision above for the case under consideration.

{'C1': '99.72%', 'C2': '0.28%'}

{'F4': 'Transmission', 'F5': 'Fuel_Type', 'F9': 'Seats', 'F2': 'Name', 'F1': 'Engine', 'F8': 'car_age', 'F3': 'Owner_Type', 'F10': 'Power', 'F6': 'Mileage', 'F7': 'Kilometers_Driven'}

{'F8': 'F4', 'F7': 'F5', 'F10': 'F9', 'F6': 'F2', 'F3': 'F1', 'F5': 'F8', 'F9': 'F3', 'F4': 'F10', 'F2': 'F6', 'F1': 'F7'}

{'C1': 'C1', 'C2': 'C2'}

Low

{'C1': 'Low', 'C2': 'High'}

KNeighborsClassifier

C1

Advertisement Prediction

The ML model or algorithm employed here predicted the class C1 with 100.0% confidence level, clearly implying that the case belongs under the class C1 and not C2 since its associated likelihood is 0.0%. Analysis of the contributions of the features indicated that only features F3 and F1 have negative influence, shifting the classification decision away from C1. However, these features are shown to be the least significant ones when it comes to assigning a label to the case under consideration. Therefore, it is a little surprising to see that the model's confidence level is very high with respect to the prediction made here. Among the remaining positive features, F4, and F5, have the strongest impact or influence, increasing the odds of C1 being the label for the case under consideration and the least positive features are F6, F7, and F2.

{'C1': '100.00%', 'C2': '0.00%'}

{'F4': 'Daily Internet Usage', 'F5': 'Daily Time Spent on Site', 'F2': 'Age', 'F6': 'Area Income', 'F7': 'ad_day', 'F1': 'ad_month', 'F3': 'Gender'}

{'F4': 'F4', 'F1': 'F5', 'F2': 'F2', 'F3': 'F6', 'F7': 'F7', 'F6': 'F1', 'F5': 'F3'}

{'C2': 'C1', 'C1': 'C2'}

Skip

{'C1': 'Skip', 'C2': 'Watch'}

BernoulliNB

C1

Hotel Satisfaction

Judging from the values of the input variables, the label predicted for the case under consideration is C1 with a high confidence level of 98.89%, implying that the probability of C2 being the actual label is just 1.11%. The attribution analysis suggests that F6, F10, and F11 are the most impactful features controlling the label selection. In contrast, F8, F15, and F4 are the least important variables whose values contribute marginally to the label selection. While the variables F6, F15, F14, and F7 contribute towards labelling the given case as C2, the remaining variables such as F10, F11, and F12 strongly support the C1 selection. The variables supporting the assignment of C1 are the positive variables whereas negative variables are those shifting the decision in favour of C2 and are against the C1 labelling decision.

{'C1': '98.89%', 'C2': '1.11%'}

{'F6': 'Type of Travel', 'F10': 'Type Of Booking', 'F11': 'Common Room entertainment', 'F12': 'Stay comfort', 'F13': 'Cleanliness', 'F1': 'Hotel wifi service', 'F7': 'Other service', 'F9': 'Ease of Online booking', 'F3': 'Age', 'F14': 'Checkin\\/Checkout service', 'F2': 'Food and drink', 'F5': 'Departure\\/Arrival convenience', 'F8': 'purpose_of_travel', 'F15': 'Hotel location', 'F4': 'Gender'}

{'F3': 'F6', 'F4': 'F10', 'F12': 'F11', 'F11': 'F12', 'F15': 'F13', 'F6': 'F1', 'F14': 'F7', 'F8': 'F9', 'F5': 'F3', 'F13': 'F14', 'F10': 'F2', 'F7': 'F5', 'F2': 'F8', 'F9': 'F15', 'F1': 'F4'}

{'C2': 'C1', 'C1': 'C2'}

dissatisfied

{'C1': 'dissatisfied', 'C2': 'satisfied'}

RandomForestClassifier

C3

Flight Price-Range Classification

There is little to no doubt that C3, among the three classes, is the proper label for this example since its associated predicted probability is 100.0%. F9, F2, and F4 are the variables with the most influence on the labelling output produced here. Furthermore, these variables have a stronger positive influence on the C3 prediction. Similarly, F7, F12, F11, F1, and F6 are some of the variables favouring the selection of C3 as the correct label. F5, F10, and F3, on the other hand, have a negative and opposing impact on the model, increasing the odds in favour of the other labels. When compared to F4, F9, and F2, all of these negative variables have a moderately low impact on the prediction given here. Finally, the lowest ranked essential input variable is recognised as F8, with a very low positive attribution.

{'C3': '100.00%', 'C1': '0.00%', 'C2': '0.00%'}

{'F2': 'Duration_hours', 'F4': 'Airline', 'F9': 'Total_Stops', 'F7': 'Journey_day', 'F5': 'Source', 'F12': 'Destination', 'F11': 'Journey_month', 'F1': 'Dep_minute', 'F6': 'Arrival_minute', 'F10': 'Arrival_hour', 'F3': 'Duration_mins', 'F8': 'Dep_hour'}

{'F7': 'F2', 'F9': 'F4', 'F12': 'F9', 'F1': 'F7', 'F10': 'F5', 'F11': 'F12', 'F2': 'F11', 'F4': 'F1', 'F6': 'F6', 'F5': 'F10', 'F8': 'F3', 'F3': 'F8'}

{'C3': 'C3', 'C2': 'C1', 'C1': 'C2'}

Low

{'C3': 'Low', 'C1': 'Moderate', 'C2': 'High'}

BernoulliNB

C1

Student Job Placement

Here, the model assigned C1 the highest probability, equal to 99.48%, implying that the predictability of C2 is only 0.52%. Per the attribution analysis, only F3 and F9 have negative contributions that decrease the likelihood of the C1 label in favour of the C2 label. F4, F12, F8, and F7 have the highest positive contributions that improve the odds in favour of the C1. The contributions of the other positive features, such as F6, F10, and F2, have moderate contributions, whilst F11, F5, and F1 are the lowest ranked positive features. All in all, the model is very certain that C2 is not the true label, and this highlighted by the fact that the joint negative contribution of F9 and F3 is only marginal when compared with the very strong influence of positive features such as F4, F8, and F7.

{'C2': '0.52%', 'C1': '99.48%'}

{'F4': 'workex', 'F8': 'specialisation', 'F7': 'ssc_p', 'F12': 'hsc_p', 'F3': 'degree_p', 'F6': 'gender', 'F10': 'degree_t', 'F2': 'etest_p', 'F11': 'hsc_b', 'F9': 'hsc_s', 'F5': 'ssc_b', 'F1': 'mba_p'}

{'F11': 'F4', 'F12': 'F8', 'F1': 'F7', 'F2': 'F12', 'F3': 'F3', 'F6': 'F6', 'F10': 'F10', 'F4': 'F2', 'F8': 'F11', 'F9': 'F9', 'F7': 'F5', 'F5': 'F1'}

{'C2': 'C2', 'C1': 'C1'}

Placed

{'C2': 'Not Placed', 'C1': 'Placed'}

RandomForestClassifier

C1

Employee Attrition

The assigned label or class by the prediction algorithm is C1, which happens to be the most probable class predicted with a probability of around 56.0%, consequently, there is a 44.0% chance that perhaps C2 could be the true label instead. The classification assertion above is attributed to the contributions of mainly F7, F16, F24, F18, F12, F19, F28, F22, F4, F23, F14, F25, F5, F15, F2, F13, F30, F17, F20, and F26. However, not all of the features are considered relevant when determining the correct label for the given case. F29, F21, F11, and F9 are examples of irrelevant features. Among the influential features, F7 and F16 are regarded as the most negative, dragging the verdict in a different direction, while the top features, F24 and F18, have positive contributions, increasing the likelihood that C2 is the right label here. Actually, the reason for the 44.0% prediction likelihood of C2 can be attributed to the strong negative influence of F7 and F16. The other negative features include F12, F19, and F4, while the other positive features are F28, F22, F23, and F14.

{'C2': '44.00%', 'C1': '56.00%'}

{'F7': 'OverTime', 'F16': 'BusinessTravel', 'F24': 'MaritalStatus', 'F18': 'JobInvolvement', 'F12': 'WorkLifeBalance', 'F19': 'Education', 'F28': 'EnvironmentSatisfaction', 'F22': 'Gender', 'F4': 'JobRole', 'F23': 'NumCompaniesWorked', 'F14': 'YearsInCurrentRole', 'F25': 'HourlyRate', 'F5': 'Department', 'F15': 'RelationshipSatisfaction', 'F2': 'PerformanceRating', 'F13': 'YearsWithCurrManager', 'F30': 'Age', 'F17': 'MonthlyRate', 'F26': 'StockOptionLevel', 'F20': 'JobSatisfaction', 'F29': 'DailyRate', 'F9': 'YearsSinceLastPromotion', 'F21': 'YearsAtCompany', 'F11': 'TrainingTimesLastYear', 'F27': 'EducationField', 'F1': 'TotalWorkingYears', 'F6': 'PercentSalaryHike', 'F10': 'MonthlyIncome', 'F8': 'JobLevel', 'F3': 'DistanceFromHome'}

{'F26': 'F7', 'F17': 'F16', 'F25': 'F24', 'F29': 'F18', 'F20': 'F12', 'F27': 'F19', 'F28': 'F28', 'F23': 'F22', 'F24': 'F4', 'F8': 'F23', 'F14': 'F14', 'F4': 'F25', 'F21': 'F5', 'F18': 'F15', 'F19': 'F2', 'F16': 'F13', 'F1': 'F30', 'F7': 'F17', 'F10': 'F26', 'F30': 'F20', 'F2': 'F29', 'F15': 'F9', 'F13': 'F21', 'F12': 'F11', 'F22': 'F27', 'F11': 'F1', 'F9': 'F6', 'F6': 'F10', 'F5': 'F8', 'F3': 'F3'}

{'C1': 'C2', 'C2': 'C1'}

Leave

{'C2': 'Leave', 'C1': 'Leave'}

GradientBoostingClassifier

C1

Broadband Sevice Signup

For the given case, the model predicts C1 as the label. The probability that the label could be the alternative class, C2, is only about 1.94% which implies that the model is very confident in this classification decision or output. F41 and F5 are the top features pushing for the C1 prediction for this case. Other features with a positive impact on this prediction include F25, F10, F23, F2, and F15. On the other hand, the values of F28, F9, F22, and F1 make up the set of features with negative attributions on the prediction decision above. However, compared to F6, F25, F10, and F5, the features above have a very marginal influence on the model. This might explain why the model is highly confident that the true label is likely C1. Finally, there were some features with insignificant impact on the model's prediction decision for the case under consideration and these include F20, F32, F16, and F3.

{'C1': '98.06%', 'C2': '1.94%'}

{'F6': 'X38', 'F5': 'X22', 'F25': 'X32', 'F10': 'X19', 'F23': 'X1', 'F15': 'X13', 'F28': 'X11', 'F2': 'X3', 'F9': 'X16', 'F22': 'X2', 'F1': 'X12', 'F38': 'X14', 'F13': 'X42', 'F36': 'X18', 'F37': 'X28', 'F41': 'X35', 'F18': 'X24', 'F12': 'X20', 'F33': 'X8', 'F34': 'X40', 'F20': 'X34', 'F32': 'X5', 'F3': 'X4', 'F16': 'X41', 'F26': 'X6', 'F19': 'X39', 'F40': 'X7', 'F14': 'X37', 'F31': 'X36', 'F11': 'X33', 'F27': 'X21', 'F21': 'X9', 'F30': 'X31', 'F7': 'X30', 'F35': 'X10', 'F4': 'X27', 'F8': 'X26', 'F24': 'X25', 'F42': 'X15', 'F39': 'X23', 'F17': 'X17', 'F29': 'X29'}

{'F35': 'F6', 'F20': 'F5', 'F29': 'F25', 'F17': 'F10', 'F40': 'F23', 'F11': 'F15', 'F9': 'F28', 'F2': 'F2', 'F14': 'F9', 'F1': 'F22', 'F10': 'F1', 'F12': 'F38', 'F38': 'F13', 'F16': 'F36', 'F26': 'F37', 'F32': 'F41', 'F22': 'F18', 'F18': 'F12', 'F6': 'F33', 'F37': 'F34', 'F31': 'F20', 'F41': 'F32', 'F3': 'F3', 'F39': 'F16', 'F4': 'F26', 'F36': 'F19', 'F5': 'F40', 'F34': 'F14', 'F33': 'F31', 'F30': 'F11', 'F19': 'F27', 'F7': 'F21', 'F28': 'F30', 'F27': 'F7', 'F8': 'F35', 'F25': 'F4', 'F24': 'F8', 'F23': 'F24', 'F13': 'F42', 'F21': 'F39', 'F15': 'F17', 'F42': 'F29'}

{'C1': 'C1', 'C2': 'C2'}

No

{'C1': 'No', 'C2': 'Yes'}

RandomForestClassifier

C1

E-Commerce Shipping

The predicted likelihood of C1 based on the information supplied to the model is 51.62%, whereas there is a 48.38% likelihood that C2 is the correct label. The uncertainty of the model in terms of this case or instance can be attributed mainly to the direction of influence of the variables F7, F10, and F3. Decreasing the chances of C1 being the correct label are the variables F7, F3, F5, and F1. While F7, F3, and F5 have strong negative attributions, F1 is the least negative variable. Increasing the likelihood of C1 prediction are mainly the variables F10, F2, and F8. The features F6, F9, and F4 also have a weak positive influence on the classification decision arrived at by the model for this case under consideration.

{'C1': '51.62%', 'C2': '48.38%'}

{'F7': 'Discount_offered', 'F10': 'Weight_in_gms', 'F3': 'Customer_care_calls', 'F5': 'Product_importance', 'F8': 'Mode_of_Shipment', 'F2': 'Warehouse_block', 'F4': 'Cost_of_the_Product', 'F9': 'Gender', 'F6': 'Customer_rating', 'F1': 'Prior_purchases'}

{'F2': 'F7', 'F3': 'F10', 'F6': 'F3', 'F9': 'F5', 'F5': 'F8', 'F4': 'F2', 'F1': 'F4', 'F10': 'F9', 'F7': 'F6', 'F8': 'F1'}

{'C1': 'C1', 'C2': 'C2'}

On-time

{'C1': 'On-time', 'C2': 'Late'}

SVC

C2

Tic-Tac-Toe Strategy

In this case, the classifier indicates that there is a 99.50% chance that the C2 class is the true label, so it is correct to conclude that the classifier is not sure that C1 is the correct label for the case here. According to the study, five input variables contradict the label choice, while four variables support the classification made above. The variables that contradict the prediction are known as negative features while positive features are those that support the classification verdict. F1, F8, F5, F2, and F3 are the negative variables that reduce the likelihood of C2 being the correct label. F4, F6, F7, and F9 are the positive variables that increase the likelihood of C2.

{'C1': '0.50%', 'C2': '99.50%'}

{'F1': 'middle-middle-square', 'F4': 'top-left-square', 'F6': 'bottom-left-square', 'F7': 'bottom-right-square', 'F8': ' top-right-square', 'F5': 'middle-right-square', 'F2': 'top-middle-square', 'F9': 'middle-left-square', 'F3': 'bottom-middle-square'}

{'F5': 'F1', 'F1': 'F4', 'F7': 'F6', 'F9': 'F7', 'F3': 'F8', 'F6': 'F5', 'F2': 'F2', 'F4': 'F9', 'F8': 'F3'}

{'C2': 'C1', 'C1': 'C2'}

player B win

{'C1': 'player B lose', 'C2': 'player B win'}

LogisticRegression

C2

Employee Promotion Prediction

Considering the values of features such as F4, F11, and F7, the model is very certain (about 99.65% certain) that C2 is the right label for the given case. While F4, F11, and F7 are the most important features, the model paid little attention to F6, F8, and F5 when deciding on the appropriate label here.Overall, driving down the odds of C2 are the negative features F7, F10, F1, and F8, which are shown to support the other label. However, the very high confidence in the above-mentioned decision is chiefly attributed to the positive contributions of F11, F4, F9, F2, and F3.

{'C2': '99.65%', 'C1': '0.35%'}

{'F11': 'avg_training_score', 'F4': 'KPIs_met >80%', 'F7': 'department', 'F9': 'age', 'F2': 'gender', 'F3': 'region', 'F10': 'length_of_service', 'F1': 'recruitment_channel', 'F6': 'previous_year_rating', 'F8': 'no_of_trainings', 'F5': 'education'}

{'F11': 'F11', 'F10': 'F4', 'F1': 'F7', 'F7': 'F9', 'F4': 'F2', 'F2': 'F3', 'F9': 'F10', 'F5': 'F1', 'F8': 'F6', 'F6': 'F8', 'F3': 'F5'}

{'C1': 'C2', 'C2': 'C1'}

Ignore

{'C2': 'Ignore', 'C1': 'Promote'}

KNeighborsClassifier

C2

Suspicious Bidding Identification

With a certainty of 100.0%, the model labels this case as C2 and from the predicted likelihoods across the classes, it can be inferred that the model verdict is that there is a zero chance that the case is under C1. The most significant feature is F2, while the least important attributes are F5, F8, and F4. The moderate features are F1, F7, F3, F6, and F9, ranked in order of their respective attributions on the label predicted. With regards to the direction of influence of each feature, some of the input features have positive attributions in favour of the assigned label and increasing the response of the model in favour of the C2 label, while the remaining ones contradict. F2, F3, F6, and F5 are the positive features, while F1, F7, F9, F8, and F4 are the negative ones, shifting the prediction verdict in the direction of C1.

{'C2': '100.00%', 'C1': '0.00%'}

{'F2': 'Z3', 'F1': 'Z9', 'F7': 'Z4', 'F3': 'Z8', 'F6': 'Z1', 'F9': 'Z5', 'F5': 'Z2', 'F8': 'Z6', 'F4': 'Z7'}

{'F3': 'F2', 'F9': 'F1', 'F4': 'F7', 'F8': 'F3', 'F1': 'F6', 'F5': 'F9', 'F2': 'F5', 'F6': 'F8', 'F7': 'F4'}

{'C2': 'C2', 'C1': 'C1'}

Normal

{'C2': 'Normal', 'C1': 'Suspicious'}

BernoulliNB

C1

Cab Surge Pricing System

The case under consideration can be labelled as either C1 or C3 or C2, and based on values for features such as F9, F10, F4, F7, and F8, the model labelled this test case as C1 with a confidence level equal to 62.29%. However, there is a 28.41% chance that the label could be C3 and a 9.3% chance that it could be C2. All the features used to make the prediction decision have different influences on the model with respect to this test case. That is, while some features positively support the prediction, others have values suggesting any of the alternative labels could be the true label. According to the analysis, F9, F4, F10, and F7 are the top features with the highest impact on the prediction made. The features F9, F4, F7, and F10 are the top attributes positively supporting the prediction of C1. In contrast, F8 and F2 are the features with the most negative attributions, pushing for the prediction of an alternative class. Further decreasing the likelihood of C1 are the features F5, F11, F3, and F1, which all negatively contribute to the model's final decision with respect to the given case. Finally, features F12 and F6 are shown to be less relevant, with positive contributions to the above classification.

{'C1': '62.29%', 'C3': '28.41%', 'C2': '9.30%'}

{'F9': 'Confidence_Life_Style_Index', 'F10': 'Destination_Type', 'F4': 'Customer_Rating', 'F7': 'Type_of_Cab', 'F8': 'Cancellation_Last_1Month', 'F2': 'Trip_Distance', 'F5': 'Var1', 'F11': 'Customer_Since_Months', 'F3': 'Gender', 'F1': 'Var3', 'F12': 'Life_Style_Index', 'F6': 'Var2'}

{'F5': 'F9', 'F6': 'F10', 'F7': 'F4', 'F2': 'F7', 'F8': 'F8', 'F1': 'F2', 'F9': 'F5', 'F3': 'F11', 'F12': 'F3', 'F11': 'F1', 'F4': 'F12', 'F10': 'F6'}

{'C2': 'C1', 'C1': 'C3', 'C3': 'C2'}

C1

{'C1': 'Low', 'C3': 'Medium', 'C2': 'High'}

SVC

C2

Advertisement Prediction

Tasked with labelling a given case as either class C2 or class C1 , the model assigns C2 as the most probable true label, with a confidence level of approximately 99.90%. This confidence level suggests that the probability of C1 being the correct label is only 0.10%. Attribution analysis conducted indicates that all the variables have a different degree of influence or contribution to the model arriving at the above mentioned classification verdict. The features responsible for the very high certainty of the model with respect to the case under consideration are F5, F3, F6, and F7. Actually, the only input variables with a negative contribution also happen to be the least relevant variables, F2 and F4.

{'C2': '99.90%', 'C1': '0.10%'}

{'F5': 'Daily Internet Usage', 'F3': 'Daily Time Spent on Site', 'F6': 'Age', 'F7': 'ad_day', 'F1': 'Area Income', 'F2': 'Gender', 'F4': 'ad_month'}

{'F4': 'F5', 'F1': 'F3', 'F2': 'F6', 'F7': 'F7', 'F3': 'F1', 'F5': 'F2', 'F6': 'F4'}

{'C1': 'C2', 'C2': 'C1'}

Skip

{'C2': 'Skip', 'C1': 'Watch'}

SVC

C2

Water Quality Classification

Even though there is moderately high confidence in the assigned label, the prediction probabilities across the two classes indicate that C1 could be the correct label for this data instance. The variables with primary contributions resulting in the labelling decision above are F9, F4, F6, and F5. As per the attribution analysis, the top two variables, F9 and F4, have a negative impact, influencing the classifier to label the given data as C1 instead of C2. The only other negative variable is F3, with moderate influence compared to the other two negative variables. On the other hand, there are many variables, specifically F6, F5, F7, F1, F2, and F8, that positively support and influence the classifier to assign C2. To a greater degree, the level of uncertainty with respect to this classification instance could be explained away by just looking at the negative variables' fairly strong pull on the classifier towards C1.

{'C1': '38.68%', 'C2': '61.32%'}

{'F9': 'Sulfate', 'F4': 'Hardness', 'F6': 'ph', 'F5': 'Conductivity', 'F3': 'Turbidity', 'F7': 'Chloramines', 'F1': 'Solids', 'F2': 'Trihalomethanes', 'F8': 'Organic_carbon'}

{'F5': 'F9', 'F2': 'F4', 'F1': 'F6', 'F6': 'F5', 'F9': 'F3', 'F4': 'F7', 'F3': 'F1', 'F8': 'F2', 'F7': 'F8'}

{'C1': 'C1', 'C2': 'C2'}

Portable

{'C1': 'Not Portable', 'C2': 'Portable'}

RandomForestClassifier

C2

Student Job Placement

According to the classification model employed here, there is a marginal chance that the true label for this test example is C1. Undoubtedly, the model estimated that the likelihood of the true label being equal to C2 is 99.92%. The above prediction decision is based on the influence of features such as F3, F2, F10, F4, and F9. All these features have significant positive support for the prediction decision here, with the top features being F2 and F10. Furthermore, the features with a moderate influence on the prediction of C2 are F8, F12, F6, and F11. While F11 positively supports labelling the case under consideration as C2, the features F8, F12, and F6 indicate otherwise. Finally, the features with marginal impact are F5, F7, and F1.

{'C1': '0.08%', 'C2': '99.92%'}

{'F2': 'ssc_p', 'F10': 'workex', 'F3': 'hsc_p', 'F4': 'specialisation', 'F9': 'gender', 'F8': 'mba_p', 'F12': 'hsc_s', 'F11': 'ssc_b', 'F6': 'degree_t', 'F5': 'hsc_b', 'F1': 'degree_p', 'F7': 'etest_p'}

{'F1': 'F2', 'F11': 'F10', 'F2': 'F3', 'F12': 'F4', 'F6': 'F9', 'F5': 'F8', 'F9': 'F12', 'F7': 'F11', 'F10': 'F6', 'F8': 'F5', 'F3': 'F1', 'F4': 'F7'}

{'C1': 'C1', 'C2': 'C2'}

Placed

{'C1': 'Not Placed', 'C2': 'Placed'}

SVM

C2

Customer Churn Modelling

Considering the values of the features, the prediction from the model for the case under consideration is C2 and this labelling decision is not 100% certain given that there is a 27.27% probability that it could be C1. For the case under consideration, the assigned label is mainly due to the values of the features F6, F1, F4, and F8 while the least important is F9. The direction of the contributions of the relevant features is summarised in the following sentences: F6 and F1 have a very strong joint positive contribution in favour of class C2 coupled with moderately positive input features F4, F8, and F5, however unlike them, F9 has a very low positive impact on the model for the case here. All of F2, F7, F10, and F3 have a negative impact on the prediction made here, however, their pull is not enough to shift the prediction in the direction of the other class label, C1.

{'C1': '27.27%', 'C2': '72.73%'}

{'F6': 'Age', 'F1': 'IsActiveMember', 'F4': 'Geography', 'F8': 'NumOfProducts', 'F5': 'Gender', 'F2': 'Tenure', 'F7': 'CreditScore', 'F10': 'EstimatedSalary', 'F3': 'Balance', 'F9': 'HasCrCard'}

{'F4': 'F6', 'F9': 'F1', 'F2': 'F4', 'F7': 'F8', 'F3': 'F5', 'F5': 'F2', 'F1': 'F7', 'F10': 'F10', 'F6': 'F3', 'F8': 'F9'}

{'C1': 'C1', 'C2': 'C2'}

Leave

{'C1': 'Stay', 'C2': 'Leave'}

DNN

C2

Concrete Strength Classification

For this case, the classification model's confidence is only about 69.40%, implying that the likelihood of label C1 is about 30.60%. According to the classification attribution analysis, F1 and F2 are the most relevant features, whereas F6 and F3 are the least influential. When the attributions of the features were carefully analysed, only F5, F8, and F4 are identified as negative features since their contributions drive down the prediction likelihood of the assigned label, C2. Conversely, F1, F2, F7, F6, and F3 have a positive influence on the model in support of labelling the given case as C2 instead of C1.

{'C2': '69.40%', 'C1': '30.60%'}

{'F1': 'slag', 'F2': 'water', 'F5': 'cement', 'F8': 'fineaggregate', 'F7': 'flyash', 'F4': 'coarseaggregate', 'F6': 'age_days', 'F3': 'superplasticizer'}

{'F2': 'F1', 'F4': 'F2', 'F1': 'F5', 'F7': 'F8', 'F3': 'F7', 'F6': 'F4', 'F8': 'F6', 'F5': 'F3'}

{'C1': 'C2', 'C2': 'C1'}

Weak

{'C2': 'Weak', 'C1': 'Strong'}

LogisticRegression

C2

Hotel Satisfaction

The model prediction for the test case is C2 and the confidence level of this prediction decision is 91.36%, while the predicted probability of C1 is only 8.64%. According to the attribution analysis, we can see that the features F2 and F7 have negative attributions, pushing the prediction decision towards the alternative label, C1. Conversely, the F13, F4, F6, and F14 have values with a positive impact, shifting the classification decision towards label C2. Furthermore, while the attributes F11 and F9 contradict the prediction made, F10 and F8 have values that support the prediction from the model for the test case under consideration. Finally, F15, F5, F1, and F12 are the least ranked features, and among them, only F12 has a negative influence that contributes marginally to the shift away from labelling the case as C2.

{'C2': '91.36%', 'C1': '8.64%'}

{'F2': 'Type of Travel', 'F7': 'Type Of Booking', 'F13': 'Hotel wifi service', 'F4': 'Common Room entertainment', 'F6': 'Stay comfort', 'F14': 'Other service', 'F11': 'Checkin\\/Checkout service', 'F10': 'Hotel location', 'F9': 'Food and drink', 'F8': 'Cleanliness', 'F3': 'Age', 'F15': 'Departure\\/Arrival convenience', 'F5': 'purpose_of_travel', 'F12': 'Ease of Online booking', 'F1': 'Gender'}

{'F3': 'F2', 'F4': 'F7', 'F6': 'F13', 'F12': 'F4', 'F11': 'F6', 'F14': 'F14', 'F13': 'F11', 'F9': 'F10', 'F10': 'F9', 'F15': 'F8', 'F5': 'F3', 'F7': 'F15', 'F2': 'F5', 'F8': 'F12', 'F1': 'F1'}

{'C2': 'C2', 'C1': 'C1'}

dissatisfied

{'C2': 'dissatisfied', 'C1': 'satisfied'}

RandomForestClassifier

C1

Student Job Placement

In summary, the model predicted an 87.14% likelihood of the class label C1 for the test example under consideration, therefore, there is a chance of about 12.86% that the correct class label could be a different label. The features with the highest impact on the model are F6, F4, F7, and F1, whose values are attributing most to the labeling decision here and among these features, only F1 shows the potential to shift the narrative toward a different label. On impact comparison, features F6, F4, F7 and F1 have higher impact on the model prediction than F11 and F5. Features F6, F4, F7, F11, and F5 show a positive impact shifting towards the prediction of C1. F1 is the most negative of all the set of features passed to the model, F12, F8, and F2 have moderate negative influence, whereas the feature F9 has very little negative impact on the prediction.

{'C1': '87.14%', 'C2': '12.86%'}

{'F6': 'ssc_p', 'F4': 'hsc_p', 'F7': 'degree_p', 'F1': 'workex', 'F11': 'specialisation', 'F5': 'gender', 'F10': 'hsc_s', 'F12': 'etest_p', 'F2': 'degree_t', 'F3': 'mba_p', 'F8': 'ssc_b', 'F9': 'hsc_b'}

{'F1': 'F6', 'F2': 'F4', 'F3': 'F7', 'F11': 'F1', 'F12': 'F11', 'F6': 'F5', 'F9': 'F10', 'F4': 'F12', 'F10': 'F2', 'F5': 'F3', 'F7': 'F8', 'F8': 'F9'}

{'C2': 'C1', 'C1': 'C2'}

Not Placed

{'C1': 'Not Placed', 'C2': 'Placed'}

LogisticRegression

C1

Food Ordering Customer Churn Prediction

Mainly based on the values of the features F40, F25, F20, and F39, the model classifies the given case as C1 with a prediction confidence level of 90.15%. This means that there is only a 9.85% chance that the correct label could be C2. The features that positively contribute to the prediction include F40, F39, F46, and F9, since their influences increase the model's response in favour of assigning the label C1. On the flip side, features dragging the final decision higher towards C2 include F25, F20, F3, and F38, since their values contradict the assigned label here. Finally, the prediction was made with less emphasis on the values of features such as F31, F26, F11, and F2, given that they are shown to have very close to zero influence.

{'C2': '9.85%', 'C1': '90.15%'}

{'F40': 'Unaffordable', 'F25': 'Perference(P2)', 'F20': 'Influence of rating', 'F39': 'Good Food quality', 'F46': 'Delay of delivery person picking up food', 'F9': 'Less Delivery time', 'F3': 'Freshness ', 'F15': 'Politeness', 'F38': 'Ease and convenient', 'F42': 'More restaurant choices', 'F35': 'Missing item', 'F29': 'Order Time', 'F33': 'Gender', 'F21': 'Time saving', 'F45': 'Unavailability', 'F17': 'Late Delivery', 'F16': 'Temperature', 'F14': 'High Quality of package', 'F36': 'Long delivery time', 'F30': 'Poor Hygiene', 'F26': 'Low quantity low time', 'F31': 'Delivery person ability', 'F11': 'Number of calls', 'F2': 'Google Maps Accuracy', 'F19': 'Residence in busy location', 'F34': 'Good Taste ', 'F10': 'Maximum wait time', 'F1': 'Influence of time', 'F41': 'Good Road Condition', 'F23': 'Age', 'F37': 'Order placed by mistake', 'F44': 'Wrong order delivered', 'F28': 'Delay of delivery person getting assigned', 'F18': 'Family size', 'F7': 'Bad past experience', 'F4': 'Health Concern', 'F12': 'Self Cooking', 'F24': 'Good Tracking system', 'F8': 'More Offers and Discount', 'F5': 'Easy Payment option', 'F43': 'Perference(P1)', 'F22': 'Educational Qualifications', 'F6': 'Monthly Income', 'F32': 'Occupation', 'F27': 'Marital Status', 'F13': 'Good Quantity'}

{'F23': 'F40', 'F9': 'F25', 'F38': 'F20', 'F15': 'F39', 'F26': 'F46', 'F39': 'F9', 'F43': 'F3', 'F42': 'F15', 'F10': 'F38', 'F12': 'F42', 'F28': 'F35', 'F31': 'F29', 'F2': 'F33', 'F11': 'F21', 'F22': 'F45', 'F19': 'F17', 'F44': 'F16', 'F40': 'F14', 'F24': 'F36', 'F20': 'F30', 'F36': 'F26', 'F37': 'F31', 'F41': 'F11', 'F34': 'F2', 'F33': 'F19', 'F45': 'F34', 'F32': 'F10', 'F30': 'F1', 'F35': 'F41', 'F1': 'F23', 'F29': 'F37', 'F27': 'F44', 'F25': 'F28', 'F7': 'F18', 'F21': 'F7', 'F18': 'F4', 'F17': 'F12', 'F16': 'F24', 'F14': 'F8', 'F13': 'F5', 'F8': 'F43', 'F6': 'F22', 'F5': 'F6', 'F4': 'F32', 'F3': 'F27', 'F46': 'F13'}

{'C2': 'C2', 'C1': 'C1'}

Go Away

{'C2': 'Return', 'C1': 'Go Away'}

SVC

C2

Bike Sharing Demand

90.58% it the predicted chance that C2 is the correct label for the given case, indicating that the predicted probability of C1 is only 9.42%. Per the feature-attributions, the top-ranked features are F10, F8, and F3, whereas the smallest important or least ranked features are F11, F7, F5, and F2. The influence of intermediate input features like F1, F12, and F6 is considered moderate. The features with positive contributions to the classification above are F3, F12, F11, and F7, while on the other hand, all the remaining features are shown to negatively contribute to the decision above. The main negative features that decrease the probability that C2 is the true label, considering the likelihood of label C1 for this case, are F10, F8, and F1.

{'C2': '90.58%', 'C1': '9.42%'}

{'F10': 'Functioning Day', 'F3': 'Solar Radiation (MJ\\/m2)', 'F8': 'Rainfall(mm)', 'F1': 'Snowfall (cm)', 'F12': 'Hour', 'F6': 'Temperature', 'F9': 'Holiday', 'F4': 'Humidity(%)', 'F11': 'Visibility (10m)', 'F7': 'Dew point temperature', 'F5': 'Seasons', 'F2': 'Wind speed (m\\/s)'}

{'F12': 'F10', 'F7': 'F3', 'F8': 'F8', 'F9': 'F1', 'F1': 'F12', 'F2': 'F6', 'F11': 'F9', 'F3': 'F4', 'F5': 'F11', 'F6': 'F7', 'F10': 'F5', 'F4': 'F2'}

{'C2': 'C2', 'C1': 'C1'}

Less than 500

{'C2': 'Less than 500', 'C1': 'More than 500'}

GradientBoostingClassifier

C2

Paris House Classification

The most likely label for the given scenario, according to this prediction, is C2, which has a prediction probability of 97.02 percent, whereas C1 has a prediction probability of just 2.98 percent. The impact of F14, F13, and F15 is mostly responsible for the aforementioned classification. F12, F7, and F2 are the following groups of features with moderate contributions. F6, F17, F5, and F1, on the other hand, receive minimal attention from the classifier. Given that all four top features have a substantial positive contribution, it's easy to see why the classifier is quite certain that C2 is the correct label in this case. F7, F9, and F4 are also negative features, having a moderate to low influence.

{'C1': '2.98%', 'C2': '97.02%'}

{'F14': 'isNewBuilt', 'F13': 'hasYard', 'F15': 'hasPool', 'F12': 'hasStormProtector', 'F7': 'made', 'F2': 'squareMeters', 'F9': 'floors', 'F4': 'cityCode', 'F3': 'hasGuestRoom', 'F10': 'basement', 'F8': 'numPrevOwners', 'F11': 'price', 'F16': 'numberOfRooms', 'F6': 'garage', 'F17': 'cityPartRange', 'F5': 'hasStorageRoom', 'F1': 'attic'}

{'F3': 'F14', 'F1': 'F13', 'F2': 'F15', 'F4': 'F12', 'F12': 'F7', 'F6': 'F2', 'F8': 'F9', 'F9': 'F4', 'F16': 'F3', 'F13': 'F10', 'F11': 'F8', 'F17': 'F11', 'F7': 'F16', 'F15': 'F6', 'F10': 'F17', 'F5': 'F5', 'F14': 'F1'}

{'C2': 'C1', 'C1': 'C2'}

Luxury

{'C1': 'Basic', 'C2': 'Luxury'}

GradientBoostingClassifier

C1

German Credit Evaluation

According to the prediction algorithm employed here, the most probable label for the given data instance is C1. The confidence level associated with the prediction decision above is 64.62%, meaning there is about a 35.38% likelihood that C2 is the right choice. The input features can be ranked according to their respective degrees of influence in decreasing order as follows: F4, F1, F6, F5, F8, F9, F3, F2, and F7. Therefore, when classifying the given case, the algorithm places little emphasis or consideration on the values of F1 and F4, however, the values of F2 and F7 are the most important here. F7, F9, F3, and F6 are regarded as negative features since their contributions decrease the likelihood of C1 being the correct label. However, positive features such as F2, F5, and F8 drive the algorithm higher towards assigning C1 to the case under consideration here.

{'C1': '64.62%', 'C2': '35.38%'}

{'F7': 'Saving accounts', 'F2': 'Sex', 'F3': 'Duration', 'F9': 'Housing', 'F8': 'Checking account', 'F5': 'Purpose', 'F6': 'Credit amount', 'F1': 'Age', 'F4': 'Job'}

{'F5': 'F7', 'F2': 'F2', 'F8': 'F3', 'F4': 'F9', 'F6': 'F8', 'F9': 'F5', 'F7': 'F6', 'F1': 'F1', 'F3': 'F4'}

{'C2': 'C1', 'C1': 'C2'}

Good Credit

{'C1': 'Good Credit', 'C2': 'Bad Credit'}

KNeighborsClassifier

C2

Tic-Tac-Toe Strategy

The true label has a 50.0% chance of being one of the two classes and based on the predicted likelihoods mentioned above, it can be concluded that the model is very unsure about the correctness of the classification. The above prediction decisions are mainly influenced by the features F8, F7, F2, F9, F4, and F5, while the least important are F1, F6, and F3. Overall, since the predicted likelihood is evenly split between the two classes, it can be concluded that the model is very uncertain as to which label is the right one. The variables with contributions that support the assignment of C2 include F8, F4, F6, and F3, but on the other hand, the ones with contributions towards the assignment of C1 are F7, F5, F2, F9, and F1. With respect to the assignment of the C2 label, F7, F5, F2, F9, and F1 are the negative variables, while F8, F4, F6, and F3 are the positive variables.

{'C2': '50.00%', 'C1': '50.00%'}

{'F8': 'middle-middle-square', 'F7': 'top-left-square', 'F5': 'bottom-left-square', 'F2': 'bottom-right-square', 'F9': 'top-middle-square', 'F4': ' top-right-square', 'F1': 'middle-right-square', 'F6': 'bottom-middle-square', 'F3': 'middle-left-square'}

{'F5': 'F8', 'F1': 'F7', 'F7': 'F5', 'F9': 'F2', 'F2': 'F9', 'F3': 'F4', 'F6': 'F1', 'F8': 'F6', 'F4': 'F3'}

{'C2': 'C2', 'C1': 'C1'}

player B lose

{'C2': 'player B lose', 'C1': 'player B win'}

DecisionTreeClassifier

C1

Credit Risk Classification

The model is assigned the label C1 for the given example. F4, F9, and F2 are the most important features that influence the above-mentioned estimate decision, however unlike them, F6, F8, and F10 are less important. The majority of features have values that swing the judgement towards the other label, C2. The only input features that increase the likelihood that C1 is the correct label are F4, F5, and F8, therefore it is very surprising that the model has 100.0% confidence in its estimate for the given example.

{'C2': '0.00%', 'C1': '100.00%'}

{'F4': 'fea_4', 'F9': 'fea_8', 'F2': 'fea_5', 'F7': 'fea_2', 'F1': 'fea_1', 'F11': 'fea_9', 'F3': 'fea_11', 'F5': 'fea_6', 'F6': 'fea_10', 'F8': 'fea_7', 'F10': 'fea_3'}

{'F4': 'F4', 'F8': 'F9', 'F5': 'F2', 'F2': 'F7', 'F1': 'F1', 'F9': 'F11', 'F11': 'F3', 'F6': 'F5', 'F10': 'F6', 'F7': 'F8', 'F3': 'F10'}

{'C2': 'C2', 'C1': 'C1'}

High

{'C2': 'Low', 'C1': 'High'}

RandomForestClassifier

C2

Annual Income Earnings

Since the probability that C1 is the correct label is only 2.18%, the classifier assigns the label C2 in this labelling instance. The main factors influencing this classification decision are the values of the variables F9, F2, F12, and F7. From inspecting the direction of influence of the above-mentioned variables, they can be referred to as the positively contributing variables because they increase the response of the classifier, increasing the odds in favour of the assigned label, C2. Other positive variables that support the prediction of C2 are F11, F1, F8, and F13, however, unlike the top positive ones, these variables have only moderate control on the classifier. Just four of all the input variables are shown to reduce the probability that C2 is the correct label and these variables are F10, F14, F6, and F5 since their respective values cause the classification judgement to shift in the direction of C1. In summary, given that the confidence level in the C2 prediction is 97.82%, it is obvious that the negative contributions of F10, F14, F6, and F5 result in only a marginal decrease in the certainty or confidence level.

{'C2': '97.82%', 'C1': '2.18%'}

{'F9': 'Capital Gain', 'F2': 'Marital Status', 'F12': 'Relationship', 'F7': 'Age', 'F8': 'Education-Num', 'F13': 'Hours per week', 'F5': 'Occupation', 'F11': 'Capital Loss', 'F6': 'Sex', 'F1': 'Education', 'F14': 'Race', 'F3': 'fnlwgt', 'F10': 'Country', 'F4': 'Workclass'}

{'F11': 'F9', 'F6': 'F2', 'F8': 'F12', 'F1': 'F7', 'F5': 'F8', 'F13': 'F13', 'F7': 'F5', 'F12': 'F11', 'F10': 'F6', 'F4': 'F1', 'F9': 'F14', 'F3': 'F3', 'F14': 'F10', 'F2': 'F4'}

{'C1': 'C2', 'C2': 'C1'}

Under 50K

{'C2': 'Under 50K', 'C1': 'Above 50K'}

RandomForestClassifier

C2

Broadband Sevice Signup

The selected case is labelled as C2 with close to an 85.0% confidence level, hinting that there is a smaller chance that it could be C1. The most important variables when determining the label for this case are F26, F18, F35, and F3. The variables with moderate influence include F2, F40, F22, and F36. However, the last three ranked variables according to their respective impacts on the model for the case under consideration are F37, F24, and F21. Significantly increasing the odds of the predicted label are the variables F26 and F35. Conversely, the F18 has the strongest impact, driving the classification verdict towards C1. Other features with similar direction of influence as F18 are F6, F22, F11, F8, and F23.

{'C1': '15.00%', 'C2': '85.00%'}

{'F26': 'X32', 'F18': 'X38', 'F35': 'X35', 'F3': 'X24', 'F28': 'X31', 'F6': 'X19', 'F2': 'X42', 'F40': 'X12', 'F22': 'X21', 'F36': 'X7', 'F9': 'X22', 'F34': 'X27', 'F7': 'X11', 'F12': 'X41', 'F41': 'X1', 'F31': 'X16', 'F11': 'X33', 'F16': 'X6', 'F8': 'X10', 'F23': 'X4', 'F4': 'X5', 'F1': 'X37', 'F10': 'X39', 'F14': 'X40', 'F25': 'X36', 'F15': 'X34', 'F19': 'X2', 'F13': 'X30', 'F27': 'X28', 'F32': 'X26', 'F42': 'X25', 'F33': 'X3', 'F29': 'X23', 'F38': 'X20', 'F20': 'X18', 'F30': 'X17', 'F39': 'X15', 'F37': 'X14', 'F24': 'X13', 'F21': 'X9', 'F5': 'X8', 'F17': 'X29'}

{'F29': 'F26', 'F35': 'F18', 'F32': 'F35', 'F22': 'F3', 'F28': 'F28', 'F17': 'F6', 'F38': 'F2', 'F10': 'F40', 'F19': 'F22', 'F5': 'F36', 'F20': 'F9', 'F25': 'F34', 'F9': 'F7', 'F39': 'F12', 'F40': 'F41', 'F14': 'F31', 'F30': 'F11', 'F4': 'F16', 'F8': 'F8', 'F3': 'F23', 'F41': 'F4', 'F34': 'F1', 'F36': 'F10', 'F37': 'F14', 'F33': 'F25', 'F31': 'F15', 'F1': 'F19', 'F27': 'F13', 'F26': 'F27', 'F24': 'F32', 'F23': 'F42', 'F2': 'F33', 'F21': 'F29', 'F18': 'F38', 'F16': 'F20', 'F15': 'F30', 'F13': 'F39', 'F12': 'F37', 'F11': 'F24', 'F7': 'F21', 'F6': 'F5', 'F42': 'F17'}

{'C1': 'C1', 'C2': 'C2'}

Yes

{'C1': 'No', 'C2': 'Yes'}

LogisticRegression

C1

Australian Credit Approval

The probable label for the given case is C1 since its associated predicted probability is 91.85% compared to the 8.15% of C2. The input variables mostly responsible for the above prediction verdict are F4, F11, and F5, however, the values of F13, F7, and F9 are deemed less relevant by the model in this case. The attributions of the input variables can be either positive or negative, depending on the direction of influence on the model. Among the variables, the ones with negative attributions that decrease the probability that C1 is the correct label are F6, F10, F14, F13, F7, and F9. On the contrary, F4, F11, F5, F2, F1, F12, and F3 are some of the remaining variables that increase the likelihood of C1 being the correct label. Based on the attributions of the variables, we can conclude that the collective impact of the negative variables is not strong enough to shift the prediction verdict away from C1, resulting in only a marginal uncertainty in the assigned label.

{'C2': '8.15%', 'C1': '91.85%'}

{'F4': 'A8', 'F11': 'A14', 'F5': 'A9', 'F6': 'A13', 'F3': 'A5', 'F10': 'A11', 'F14': 'A12', 'F2': 'A7', 'F1': 'A4', 'F12': 'A10', 'F8': 'A6', 'F13': 'A1', 'F7': 'A2', 'F9': 'A3'}

{'F8': 'F4', 'F14': 'F11', 'F9': 'F5', 'F13': 'F6', 'F5': 'F3', 'F11': 'F10', 'F12': 'F14', 'F7': 'F2', 'F4': 'F1', 'F10': 'F12', 'F6': 'F8', 'F1': 'F13', 'F2': 'F7', 'F3': 'F9'}

{'C2': 'C2', 'C1': 'C1'}

Class 2

{'C2': 'Class 1', 'C1': 'Class 2'}

KNeighborsClassifier

C2

Tic-Tac-Toe Strategy

The classification verdict of the model for the case under consideration has a 50.10% chance of being C1. But based on the estimated likelihoods indicated above, it is possible to deduce that the model is extremely doubtful about the classification's validity. The following variables have the most attributions to the aforementioned prediction decisions: F3, F4, F9, and F8. F5 and F1 are the least important, whereas the values of the variables F7, F6, and F2 had only a moderate impact. Regarding the direction of influence of the variables, F3, F6, F5, and F1 are the ones driving the classification higher towards the C1 label and away from C2. However, factoring the likelihood of the C2 label, the negative variables, F4, F8, F9, F7, and F2, successfully cast doubt on the validity of the assigned label. In simple terms, the negative contributions from F4, F8, and F9 can easily explain the uncertainty associated with the class label assignment for the case under consideration here.

{'C1': '50.10%', 'C2': '49.90%'}

{'F3': 'middle-middle-square', 'F4': 'top-left-square', 'F8': 'bottom-left-square', 'F9': 'bottom-right-square', 'F7': 'top-middle-square', 'F6': ' top-right-square', 'F2': 'middle-right-square', 'F5': 'bottom-middle-square', 'F1': 'middle-left-square'}

{'F5': 'F3', 'F1': 'F4', 'F7': 'F8', 'F9': 'F9', 'F2': 'F7', 'F3': 'F6', 'F6': 'F2', 'F8': 'F5', 'F4': 'F1'}

{'C1': 'C1', 'C2': 'C2'}

player B lose

{'C1': 'player B win', 'C2': 'player B lose'}

LogisticRegression

C2

Used Cars Price-Range Prediction

The algorithm classified the given data as C2 with close to 99.32% certainty since the prediction likelihood of C1 is only 0.68%. The abovementioned prediction verdict is largely due to the influence of F6, F1, and F5 while the other influential features include F7, F4, and F9. However, F10, F3, F8, and F2 are shown to have smaller contributions to the decision made here. Not all the features have positive contributions, and F5, F4, and F9 are known as negative features since for the given case, they reduce the likelihood of the assigned label and hence they favour or support labelling the case as C1 instead.

{'C1': '0.68%', 'C2': '99.32%'}

{'F6': 'Power', 'F1': 'car_age', 'F5': 'Name', 'F7': 'Fuel_Type', 'F4': 'Seats', 'F9': 'Transmission', 'F10': 'Mileage', 'F3': 'Owner_Type', 'F8': 'Kilometers_Driven', 'F2': 'Engine'}

{'F4': 'F6', 'F5': 'F1', 'F6': 'F5', 'F7': 'F7', 'F10': 'F4', 'F8': 'F9', 'F2': 'F10', 'F9': 'F3', 'F1': 'F8', 'F3': 'F2'}

{'C1': 'C1', 'C2': 'C2'}

High

{'C1': 'Low', 'C2': 'High'}

LogisticRegression

C2

Flight Price-Range Classification

The model is quite certain that C2 is the most likely class for the current scenario. C2 has a 90.48% chance of being correct, implying that any of the other labels is highly unlikely. F4 and F7 are the most relevant variables influencing the abovementioned classification decision but all other factors or variables are proven to have a moderate or minor influence. Fortunately, the top variables, F7 and F4, have an impact on the model that is positive, boosting the chance of C2. Furthermore, whereas F3 and F9 force the model to forecast C2, the variables F8, F1, F6, and F10 are forcing the model to assign a different label. Finally, several variables have a very minor influence on the model's final forecast here, but F11, F6, and F10 are shown to have the least contributions.

{'C2': '90.48%', 'C3': '9.51%', 'C1': '0.01%'}

{'F7': 'Total_Stops', 'F4': 'Airline', 'F9': 'Destination', 'F3': 'Arrival_hour', 'F8': 'Source', 'F2': 'Duration_hours', 'F5': 'Dep_hour', 'F1': 'Dep_minute', 'F12': 'Arrival_minute', 'F11': 'Journey_month', 'F6': 'Journey_day', 'F10': 'Duration_mins'}

{'F12': 'F7', 'F9': 'F4', 'F11': 'F9', 'F5': 'F3', 'F10': 'F8', 'F7': 'F2', 'F3': 'F5', 'F4': 'F1', 'F6': 'F12', 'F2': 'F11', 'F1': 'F6', 'F8': 'F10'}

{'C1': 'C2', 'C3': 'C3', 'C2': 'C1'}

Low

{'C2': 'Low', 'C3': 'Moderate', 'C1': 'High'}

LogisticRegression

C1

Suspicious Bidding Identification

The label assigned by the model is C1 with a higher predicted confidence level of 99.99%, meaning the probability of C2 being the correct label is virtually equal to zero. The classification decision above is mainly due to the influence of the features F8, F2, F7, and F6, however, the remaining features have very marginal contributions to the decision. Among the features, only F9 and F1 are shown to have a negative impact, reducing the likelihood of the assigned label. However, this negative influence is very weak compared to that of the top positive features, F8, F2, F7, and F6.

{'C1': '99.99%', 'C2': '0.01%'}

{'F8': 'Z3', 'F2': 'Z8', 'F7': 'Z4', 'F6': 'Z2', 'F4': 'Z5', 'F3': 'Z7', 'F9': 'Z1', 'F5': 'Z6', 'F1': 'Z9'}

{'F3': 'F8', 'F8': 'F2', 'F4': 'F7', 'F2': 'F6', 'F5': 'F4', 'F7': 'F3', 'F1': 'F9', 'F6': 'F5', 'F9': 'F1'}

{'C2': 'C1', 'C1': 'C2'}

Normal

{'C1': 'Normal', 'C2': 'Suspicious'}

SVC

C2

German Credit Evaluation

With respect to the given case, the classification algorithm employed here generates C2 as the most probable class since the probability of C1 is 41.63% while that of C2 is 58.37%. F7, F5, and F3 are the most influential features resulting in the classification decision mentioned above, whereas the least relevant features are F2 and F1. As indicated by the prediction probabilities across the classes, the confidence in the labelling decision here is not perfect, which can be attributed to the influence of the negative features F7, F5, F3, and F4. On the other hand, the moderate positive influence of F6, F8, F9, F2, and F1 explains the algorithm's decision to label the case as C2 with such an average level of confidence.

{'C2': '58.37%', 'C1': '41.63%'}

{'F7': 'Checking account', 'F5': 'Duration', 'F3': 'Saving accounts', 'F8': 'Sex', 'F6': 'Purpose', 'F9': 'Age', 'F4': 'Housing', 'F2': 'Job', 'F1': 'Credit amount'}

{'F6': 'F7', 'F8': 'F5', 'F5': 'F3', 'F2': 'F8', 'F9': 'F6', 'F1': 'F9', 'F4': 'F4', 'F3': 'F2', 'F7': 'F1'}

{'C1': 'C2', 'C2': 'C1'}

Good Credit

{'C2': 'Good Credit', 'C1': 'Bad Credit'}

GradientBoostingClassifier

C2

German Credit Evaluation

The most likely label for the provided data instance, according to the predictive algorithm used here, is C2. The confidence level associated with the above prediction decision is 64.62 percent, which means C1 has a 35.38 percent chance of being correct. The following input features can be prioritised in decreasing order according to their relative degrees of influence: F1, F3, F7, F5, F2, F4, F9, F6, and F8. As a result, the algorithm places little emphasis or attention on the values of F3 and F1 when classifying the given case whilst the values of F6, F9, and F8 are the most relevant. Regarding the direction of influence or impact of the input features, F8, F4, F9, and F7 are considered negative features because their contributions reduce the likelihood of C2 being the correct label. Positive features such as F2, F5, and F6, however, push the algorithm closer to assigning C2 to the situation in question.

{'C2': '64.62%', 'C1': '35.38%'}

{'F8': 'Saving accounts', 'F6': 'Sex', 'F9': 'Duration', 'F4': 'Housing', 'F2': 'Checking account', 'F5': 'Purpose', 'F7': 'Credit amount', 'F3': 'Age', 'F1': 'Job'}

{'F5': 'F8', 'F2': 'F6', 'F8': 'F9', 'F4': 'F4', 'F6': 'F2', 'F9': 'F5', 'F7': 'F7', 'F1': 'F3', 'F3': 'F1'}

{'C1': 'C2', 'C2': 'C1'}

Good Credit

{'C2': 'Good Credit', 'C1': 'Bad Credit'}

SVC

C1

Real Estate Investment

C1 is the label predicted by the classifier for the case or example under consideration the confidence in the above prediction is about 96.35%. It is important to take into consideration, however, that there is also a very small chance equal to 3.65% that the correct label could be C2. The ranking of the features according to their respective contributions to the decision above is as follows: The top features with significant influences are F4, F12, and F11. The remaining features with moderate contributions are: F10, F3, F8, F1, F16, F20, F15, F2, F19, F5, F14, F7, and F17. Finally, the values of F6, F9, F13, and F18 are shown to have a very low impact on the prediction of C1 for the case under consideration. The assessment below only considers the features shown to have the most relevant impact in terms of the direction of the prediction here. Among the most contributing features, only F10 and F3 have a negative influence, while the remaining ones, F4, F12, F11, and F8, are shown to have positive contributions to the prediction for the case. Looking at the cumulative influences of each set of positive and negative features, it is not strange that the label assigned is C1 with a confidence level of 96.35%.

{'C2': '3.65%', 'C1': '96.35%'}

{'F4': 'Feature7', 'F12': 'Feature4', 'F11': 'Feature14', 'F10': 'Feature2', 'F3': 'Feature8', 'F8': 'Feature1', 'F1': 'Feature13', 'F16': 'Feature6', 'F20': 'Feature10', 'F15': 'Feature15', 'F2': 'Feature18', 'F19': 'Feature9', 'F5': 'Feature12', 'F14': 'Feature16', 'F7': 'Feature19', 'F17': 'Feature5', 'F6': 'Feature11', 'F9': 'Feature20', 'F13': 'Feature3', 'F18': 'Feature17'}

{'F11': 'F4', 'F9': 'F12', 'F17': 'F11', 'F1': 'F10', 'F3': 'F3', 'F7': 'F8', 'F16': 'F1', 'F10': 'F16', 'F13': 'F20', 'F4': 'F15', 'F19': 'F2', 'F12': 'F19', 'F15': 'F5', 'F18': 'F14', 'F5': 'F7', 'F2': 'F17', 'F14': 'F6', 'F20': 'F9', 'F8': 'F13', 'F6': 'F18'}

{'C2': 'C2', 'C1': 'C1'}

Invest

{'C2': 'Ignore', 'C1': 'Invest'}

DecisionTreeClassifier

C1

Insurance Churn

The model predicted the C1 class with very high confidence of 93.27%, hence we can conclude that there is only a 6.73% chance that the true label is C2. Two features have a very strong positive influence on the prediction of the C1 class and they are F3 and F5. The following features have a medium impact and are listed in decreasing order of influence: F1 and F10 have a negative influence, while F15 and F16 have a positive influence on the prediction of C1. F16, F12, and F8 have a positive influence on the prediction of the C1 class, while F6, F11, F4, and F14 influence the prediction negatively. Those with the least contribution regarding the model's decision for this case are shown to be F9, F7, F8, and F2. Among these least contributing features F9 and F2 are shown to have negative contributions whereas F8 and F7 contribute positively in favour of the assigned label.

{'C2': '6.73%', 'C1': '93.27%'}

{'F3': 'feature15', 'F5': 'feature14', 'F1': 'feature10', 'F10': 'feature11', 'F15': 'feature5', 'F16': 'feature13', 'F6': 'feature4', 'F14': 'feature3', 'F11': 'feature12', 'F4': 'feature1', 'F12': 'feature7', 'F13': 'feature2', 'F8': 'feature6', 'F9': 'feature0', 'F7': 'feature9', 'F2': 'feature8'}

{'F9': 'F3', 'F8': 'F5', 'F4': 'F1', 'F5': 'F10', 'F15': 'F15', 'F7': 'F16', 'F14': 'F6', 'F13': 'F14', 'F6': 'F11', 'F11': 'F4', 'F1': 'F12', 'F12': 'F13', 'F16': 'F8', 'F10': 'F9', 'F3': 'F7', 'F2': 'F2'}

{'C2': 'C2', 'C1': 'C1'}

Leave

{'C2': 'Stay', 'C1': 'Leave'}

BernoulliNB

C2

Customer Churn Modelling

This case or instance is labelled as C2 with a very high confidence level, however, the classifier estimates that C1 could be the correct label with a prediction likelihood of about 5.75%. The values F9, F5, and F8 played a major role in the aforementioned labelling choice and because F10 and F2 have minimal attributions, they are the lowest rated features. F5 and F9 have values, which increases the probability that C2 is the correct label. Other variables that drive the clasifier towards assigning the predicted class are F3 and F1. Here, F5, F1, F9, and F3, are referred to as positive input variables since their contributions are towards the generated C2 label. In contrast, the remaining six variables are shown to have a negative influence on the classifier, indicating that the correct label could be C1 instead of the C2 selected by the classifier and the strongest negative variables are F8, F7, and F4.

{'C2': '94.25%', 'C1': '5.75%'}

{'F9': 'IsActiveMember', 'F5': 'NumOfProducts', 'F8': 'Gender', 'F7': 'Geography', 'F4': 'Age', 'F6': 'CreditScore', 'F3': 'EstimatedSalary', 'F1': 'Balance', 'F10': 'HasCrCard', 'F2': 'Tenure'}

{'F9': 'F9', 'F7': 'F5', 'F3': 'F8', 'F2': 'F7', 'F4': 'F4', 'F1': 'F6', 'F10': 'F3', 'F6': 'F1', 'F8': 'F10', 'F5': 'F2'}

{'C1': 'C2', 'C2': 'C1'}

Stay

{'C2': 'Stay', 'C1': 'Leave'}

LogisticRegression

C1

Printer Sales

The prediction likelihood of class C1 is 73.85%, making it the most probable label for the given case. When making the above prediction, the input features are shown to have some degree of influence on the decision made by the classifier. While features such as F21, F17, and F9 have very low contributions to the classification, the features F11 and F26 are shown to be the main contributors to the decision. Finally, the features with moderate contributions are 21, F3, 42, F5, F16, and F7. As indicated by the prediction likelihoods across the classes, the classifier is shown to have a little doubt in the correctness or validity of C1, and the main features resulting in this little uncertainty are the negative features F11, F18, F7, F16, F14, F25, and F6. However, the values of F26, F3, F20, F5, F12, and F2 suggest that C1 is very likely the true label.

{'C1': '73.85%', 'C2': '26.15%'}

{'F26': 'X8', 'F11': 'X1', 'F3': 'X24', 'F20': 'X21', 'F18': 'X4', 'F7': 'X18', 'F5': 'X17', 'F16': 'X25', 'F2': 'X7', 'F14': 'X20', 'F25': 'X23', 'F12': 'X9', 'F15': 'X2', 'F8': 'X22', 'F24': 'X16', 'F22': 'X10', 'F4': 'X15', 'F10': 'X14', 'F6': 'X26', 'F13': 'X19', 'F21': 'X13', 'F17': 'X12', 'F9': 'X11', 'F1': 'X6', 'F19': 'X5', 'F23': 'X3'}

{'F8': 'F26', 'F1': 'F11', 'F24': 'F3', 'F21': 'F20', 'F4': 'F18', 'F18': 'F7', 'F17': 'F5', 'F25': 'F16', 'F7': 'F2', 'F20': 'F14', 'F23': 'F25', 'F9': 'F12', 'F2': 'F15', 'F22': 'F8', 'F16': 'F24', 'F10': 'F22', 'F15': 'F4', 'F14': 'F10', 'F26': 'F6', 'F19': 'F13', 'F13': 'F21', 'F12': 'F17', 'F11': 'F9', 'F6': 'F1', 'F5': 'F19', 'F3': 'F23'}

{'C1': 'C1', 'C2': 'C2'}

Less

{'C1': 'Less', 'C2': 'More'}

LogisticRegression

C2

Printer Sales

According to the model, the probability of C1 is 12.35% and that of C2 is 87.65% meaning C2 is the most probable label for the given case. The variables with the majority influence on the abovementioned decision are F23, F22, F12, F8, F16, and F4 whereas variables F14, F13, F11, F18, F19, and F6 are shown to have little to no influence on the model's decision with respect to the given case. The contributions and influence of variables such as F24, F10, F9, and F7 can be described as moderate. Among the variables controlling the prediction decision here, F23, F16, F10, F7, F2, F21, F20, and F3 are the negative variables decreasing the model's response to the output of the label C2. Conversely, the highly influential variables F22, F12, F8, and F4 are the main drivers that contribute positively, increasing the probability of C2 being the correct class label. Overall, given that the variable with the highest influence on the model is F23, a negative variable, it is not unexpected that there is a little doubt in the classification decision here, as shown by the prediction probabilities across the classes.

{'C1': '12.35%', 'C2': '87.65%'}

{'F23': 'X8', 'F22': 'X21', 'F12': 'X1', 'F8': 'X25', 'F16': 'X2', 'F4': 'X24', 'F24': 'X15', 'F10': 'X4', 'F1': 'X20', 'F9': 'X10', 'F7': 'X5', 'F15': 'X6', 'F2': 'X11', 'F21': 'X12', 'F20': 'X9', 'F17': 'X26', 'F25': 'X23', 'F5': 'X7', 'F26': 'X14', 'F3': 'X17', 'F14': 'X18', 'F13': 'X19', 'F11': 'X16', 'F18': 'X13', 'F19': 'X22', 'F6': 'X3'}

{'F8': 'F23', 'F21': 'F22', 'F1': 'F12', 'F25': 'F8', 'F2': 'F16', 'F24': 'F4', 'F15': 'F24', 'F4': 'F10', 'F20': 'F1', 'F10': 'F9', 'F5': 'F7', 'F6': 'F15', 'F11': 'F2', 'F12': 'F21', 'F9': 'F20', 'F26': 'F17', 'F23': 'F25', 'F7': 'F5', 'F14': 'F26', 'F17': 'F3', 'F18': 'F14', 'F19': 'F13', 'F16': 'F11', 'F13': 'F18', 'F22': 'F19', 'F3': 'F6'}

{'C2': 'C1', 'C1': 'C2'}

More

{'C1': 'Less', 'C2': 'More'}

RandomForestClassifier

C3

Cab Surge Pricing System

With a moderately high level of confidence, C3 is assigned to the given case by the classifier and this is due to the fact that the other classes, C2 and C1, have likelihoods of 3.0% and 14.0%, respectively. Across the input features, only F2, F7, F10, and F5 are shown to contribute negatively, shifting the classification away from C3 and towards C2 and C1. On the contrary, the features such as F1, F12, F3, and F8 are among the positive set of features that drive the verdict in support of assigning C3 to the given case. From the attributions of the different features, F1 is the most relevant contributor to the classification made here, while F9, F11, and F4 are ranked as the least influential features and considering the direction of influence of each input feature, it is understandable why the classifier is certain about the decision made.

{'C2': '3.00%', 'C3': '83.00%', 'C1': '14.00%'}

{'F1': 'Type_of_Cab', 'F2': 'Destination_Type', 'F7': 'Trip_Distance', 'F10': 'Cancellation_Last_1Month', 'F12': 'Confidence_Life_Style_Index', 'F3': 'Var3', 'F5': 'Customer_Since_Months', 'F8': 'Life_Style_Index', 'F6': 'Var2', 'F9': 'Gender', 'F11': 'Var1', 'F4': 'Customer_Rating'}

{'F2': 'F1', 'F6': 'F2', 'F1': 'F7', 'F8': 'F10', 'F5': 'F12', 'F11': 'F3', 'F3': 'F5', 'F4': 'F8', 'F10': 'F6', 'F12': 'F9', 'F9': 'F11', 'F7': 'F4'}

{'C3': 'C2', 'C1': 'C3', 'C2': 'C1'}

C2

{'C2': 'Low', 'C3': 'Medium', 'C1': 'High'}

MLPClassifier

C1

Ethereum Fraud Detection

Considering the values of the input variables, the classification model is very confident that the most probable label is not C2 but C1. The top input variables receiving much consideration from the model to arrive at the classification verdict are F27, F4, F19, F38, and F36. Among these most influential variables, F27 and F4 are regarded as negatives since their contributions serve to swing the classification decision in the opposite direction. On the contrary, F19, F38, and F36 have a positive influence, increasing the model's response to favour labelling the given case as C1. Other positive variables include F25, F5, and F3, whereas the other negative ones include F24, F29, and F8. Input variables such as F23, F2, F37, and F6 are shown to have zero attributions, that is, their values are not paid enough attention to influence the model's decision with respect to the given case.

{'C1': '100.00%', 'C2': '0.00%'}

{'F27': 'Unique Received From Addresses', 'F4': ' ERC20 total Ether sent contract', 'F19': 'total ether received', 'F38': 'Number of Created Contracts', 'F36': 'Sent tnx', 'F24': ' ERC20 uniq rec token name', 'F29': ' ERC20 uniq rec contract addr', 'F5': 'max value received ', 'F25': 'total transactions (including tnx to create contract', 'F8': ' ERC20 uniq sent addr.1', 'F3': ' ERC20 uniq sent addr', 'F28': 'Received Tnx', 'F13': ' ERC20 uniq rec addr', 'F21': 'avg val sent', 'F15': 'min value received', 'F22': 'Unique Sent To Addresses', 'F32': ' ERC20 uniq sent token name', 'F18': ' Total ERC20 tnxs', 'F26': 'Time Diff between first and last (Mins)', 'F35': 'Avg min between received tnx', 'F23': 'total Ether sent', 'F2': 'min val sent', 'F37': 'avg val received', 'F6': ' ERC20 avg val sent', 'F30': ' ERC20 max val sent', 'F12': ' ERC20 min val sent', 'F17': ' ERC20 avg val rec', 'F16': ' ERC20 max val rec', 'F10': ' ERC20 min val rec', 'F31': 'max val sent', 'F33': 'min value sent to contract', 'F11': 'max val sent to contract', 'F1': ' ERC20 total ether sent', 'F34': ' ERC20 total Ether received', 'F14': 'avg value sent to contract', 'F7': 'total ether balance', 'F20': 'total ether sent contracts', 'F9': 'Avg min between sent tnx'}

{'F7': 'F27', 'F26': 'F4', 'F20': 'F19', 'F6': 'F38', 'F4': 'F36', 'F38': 'F24', 'F30': 'F29', 'F10': 'F5', 'F18': 'F25', 'F29': 'F8', 'F27': 'F3', 'F5': 'F28', 'F28': 'F13', 'F14': 'F21', 'F9': 'F15', 'F8': 'F22', 'F37': 'F32', 'F23': 'F18', 'F3': 'F26', 'F2': 'F35', 'F19': 'F23', 'F12': 'F2', 'F11': 'F37', 'F36': 'F6', 'F35': 'F30', 'F34': 'F12', 'F33': 'F17', 'F32': 'F16', 'F31': 'F10', 'F13': 'F31', 'F15': 'F33', 'F16': 'F11', 'F25': 'F1', 'F24': 'F34', 'F17': 'F14', 'F22': 'F7', 'F21': 'F20', 'F1': 'F9'}

{'C1': 'C1', 'C2': 'C2'}

Not Fraud

{'C1': 'Not Fraud', 'C2': 'Fraud'}

SVC

C1

Job Change of Data Scientists

The odds are in favour of label C1 given that the probability of it being the correct label for the case under consideration is 81.32%. However, the likelihood of label C2 is 18.68%. The classification decision above is mainly due to the values of F7, F11, F3, and F4. The feature with the least attribution to the model's output label here is F10. The features F7, F3, and F11 have very strong positive contributions to the prediction, increasing the odds of the label C1. Other features with positive attribution in support of C1 are F8, F6, and F2. Unlike the features stated above, the remaining features, F4, F1, F9, F12, F5, and F10, have values that shift the final prediction verdict in the direction of C2 and account for its 18.68% likelihood.

{'C1': '81.32%', 'C2': '18.68%'}

{'F7': 'city', 'F11': 'company_type', 'F3': 'city_development_index', 'F4': 'education_level', 'F1': 'enrolled_university', 'F9': 'gender', 'F8': 'relevent_experience', 'F12': 'training_hours', 'F5': 'major_discipline', 'F6': 'company_size', 'F2': 'experience', 'F10': 'last_new_job'}

{'F3': 'F7', 'F11': 'F11', 'F1': 'F3', 'F7': 'F4', 'F6': 'F1', 'F4': 'F9', 'F5': 'F8', 'F2': 'F12', 'F8': 'F5', 'F10': 'F6', 'F9': 'F2', 'F12': 'F10'}

{'C1': 'C1', 'C2': 'C2'}

Stay

{'C1': 'Stay', 'C2': 'Leave'}

DNN

C2

Concrete Strength Classification

The model predicted C2 with a high probability equal to 88.70%, whereas C1 has only a 11.30% likelihood of being the true label. Considering the predicted likelihood of C1, there is only little confidence in its correctness as the true label for the case here. The value of F7 has a large negative influence on the C2 classification decision, while F6 is the top positive feature. F2, F8, F5, and F4 all have positive impacts on the C2 prediction, with F2 and F6 having the highest influence, F5 and F4 having low influence, and F8 being somewhere in the middle. Broadly speaking, the negative influences of F7, F1, and F3 only succeed in driving the decision slightly away from C2 towards the other label as shown by the predicted probabilities across the classes.

{'C2': '88.70%', 'C1': '11.30%'}

{'F7': 'coarseaggregate', 'F6': 'age_days', 'F2': 'superplasticizer', 'F8': 'cement', 'F5': 'water', 'F4': 'fineaggregate', 'F3': 'slag', 'F1': 'flyash'}

{'F6': 'F7', 'F8': 'F6', 'F5': 'F2', 'F1': 'F8', 'F4': 'F5', 'F7': 'F4', 'F2': 'F3', 'F3': 'F1'}

{'C2': 'C2', 'C1': 'C1'}

Weak

{'C2': 'Weak', 'C1': 'Strong'}

BernoulliNB

C1

Suspicious Bidding Identification

The algorithm's predicted output label for the given case is C1 with a very strong confidence level equal to 100.0%; hence C2 can't be the true label. Among the features, the most relevant ones are F6, F8, and F2 with very significant impact, pushing the prediction decision away from C2 towards C1. The next set of attributes, F7, F5, and F9, offer a moderate shift towards C1 coupled with marginal positive contribution from F4 and F1. From the above statements, all the features are shown to support the label assignment decision in the case under consideration. Consequently, it is no wonder that the algorithm has 100.0% confidence in the output decision or verdict above.

{'C1': '100.00%', 'C2': '0.00%'}

{'F6': 'Z3', 'F8': 'Z8', 'F2': 'Z2', 'F7': 'Z7', 'F3': 'Z5', 'F5': 'Z4', 'F9': 'Z6', 'F4': 'Z1', 'F1': 'Z9'}

{'F3': 'F6', 'F8': 'F8', 'F2': 'F2', 'F7': 'F7', 'F5': 'F3', 'F4': 'F5', 'F6': 'F9', 'F1': 'F4', 'F9': 'F1'}

{'C2': 'C1', 'C1': 'C2'}

Normal

{'C1': 'Normal', 'C2': 'Suspicious'}

RandomForestClassifier

C2

Health Care Services Satisfaction Prediction

In this case, the prediction algorithm is not 100.0% certain that the correct label for the given case is C2, since there is a 43.49% chance that the right label could be C1 instead. The algorithm's decision to label the case as C2 mainly stems from the influence of features such as F7, F5, F14, F12, and F10. On the other hand, little consideration is paid to the values of the least ranked features, F16, F15, and F4. Within the top-ranked features, F10 and F12 have a negative impact, increasing the prediction probability of label C1. Further decreasing the likelihood of the C2 class are the negative features are F6, F1, F3, and F4. However, all the remaining features strongly or moderately push for the classification output to be C2 and the notable positive features are F7, F5, and F14. Considering all the features' attributions, the uncertainty or doubt in the classification could be attributed to the algorithm's paying too much attention to the values of the negative features.

{'C1': '43.49%', 'C2': '56.51%'}

{'F7': 'Quality\\/experience dr.', 'F5': 'Exact diagnosis', 'F14': 'Hygiene and cleaning', 'F10': 'Specialists avaliable', 'F12': 'Modern equipment', 'F11': 'hospital rooms quality', 'F2': 'Admin procedures', 'F9': 'avaliablity of drugs', 'F6': 'parking, playing rooms, caffes', 'F1': 'Time waiting', 'F3': 'friendly health care workers', 'F8': 'Communication with dr', 'F13': 'waiting rooms', 'F16': 'Check up appointment', 'F15': 'lab services', 'F4': 'Time of appointment'}

{'F6': 'F7', 'F9': 'F5', 'F4': 'F14', 'F7': 'F10', 'F10': 'F12', 'F15': 'F11', 'F3': 'F2', 'F13': 'F9', 'F16': 'F6', 'F2': 'F1', 'F11': 'F3', 'F8': 'F8', 'F14': 'F13', 'F1': 'F16', 'F12': 'F15', 'F5': 'F4'}

{'C1': 'C1', 'C2': 'C2'}

Satisfied

{'C1': 'Dissatisfied', 'C2': 'Satisfied'}

LogisticRegression

C3

Flight Price-Range Classification

Mainly based on the information on the case given, the classifier's output decision is as follows: C3 is the most probable label, followed by C2 and C1, with C1 being the least. To be specific, the prediction probabilities across the classes are as follows: C1 has 4.34%, C2 has 21.64%, and C3 has 74.0% chance of being the true label. The moderately high classification confidence is largely due to the impact of F12, F10, and F6. However, the values of F2 and F9 received very little consideration when the classifier was picking the most probable label for the given case. With respect to the direction of influence of the features, F12, F10, F7, F3, and F9 have varying degrees of positive contributions, driving the classifier to label the case as C3. On the contrary, F6, F1, F11, and F4 are among the negative features, shifting the classification decision in a direction away from C3.

{'C1': '4.34%', 'C2': '21.64%', 'C3': '74.02%'}

{'F12': 'Airline', 'F10': 'Total_Stops', 'F6': 'Source', 'F1': 'Arrival_minute', 'F11': 'Arrival_hour', 'F4': 'Dep_minute', 'F8': 'Duration_hours', 'F5': 'Journey_month', 'F7': 'Journey_day', 'F3': 'Duration_mins', 'F2': 'Destination', 'F9': 'Dep_hour'}

{'F9': 'F12', 'F12': 'F10', 'F10': 'F6', 'F6': 'F1', 'F5': 'F11', 'F4': 'F4', 'F7': 'F8', 'F2': 'F5', 'F1': 'F7', 'F8': 'F3', 'F11': 'F2', 'F3': 'F9'}

{'C2': 'C1', 'C3': 'C2', 'C1': 'C3'}

High

{'C1': 'Low', 'C2': 'Moderate', 'C3': 'High'}