def CrossValidationFolds_Traversal(estimator, vdataset):
    "\n    Arguments:    \n        - estimator  = classifer of model\n        - vdataset =  vehicld dataset \n    \n    This function computes acccuracy score with the\n    Cross Validation Score for each KFold with K from 2 to 10 \n    \n    Output:\n        returns matrix conatining value of K with it's corresponding performance score\n    "
    X = vdataset.drop(['Class', 'Class_code'], axis=1)
    y = vdataset['Class_code']
    scores = []
    matrix = pd.DataFrame(columns=['KFold', 'Accuracy'])
    for i in range(2, 11):
        score = cross_val_score(estimator, X, y, cv=i, scoring='accuracy')
        scores.append(score.mean())
        matrix = matrix.append({'KFold': i, 'Accuracy': (score.mean() * 100)}, ignore_index=True)
    return matrix
Arguments:    
    - estimator  = classifer of model
    - vdataset =  vehicld dataset 

This function computes acccuracy score with the
Cross Validation Score for each KFold with K from 2 to 10 

Output:
    returns matrix conatining value of K with it's corresponding performance score
def CrossValidationFolds_Traversal(estimator, vdataset):
    "\n    Arguments:    \n        - estimator  = classifer of model\n        - vdataset =  vehicld dataset \n    \n    This function computes acccuracy score with the\n    Cross Validation Score for each KFold with K from 2 to 10 \n    \n    Output:\n        returns matrix conatining value of K with it's corresponding performance score\n    "
    X = vdataset.drop(['Class', 'Class_code'], axis=1)
    y = vdataset['Class_code']
    scores = []
    matrix = pd.DataFrame(columns=['KFold', 'Accuracy'])
    for i in range(2, 11):
        score = cross_val_score(estimator, X, y, cv=i, scoring='accuracy')
        scores.append(score.mean())
        matrix = matrix.append({'KFold': i, 'Accuracy': (score.mean() * 100)}, ignore_index=True)
    return matrix