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| 1 |
+
---
|
| 2 |
+
library_name: sklearn
|
| 3 |
+
tags:
|
| 4 |
+
- sklearn
|
| 5 |
+
- skops
|
| 6 |
+
- tabular-classification
|
| 7 |
+
widget:
|
| 8 |
+
structuredData:
|
| 9 |
+
attribute_0:
|
| 10 |
+
- material_7
|
| 11 |
+
- material_7
|
| 12 |
+
- material_7
|
| 13 |
+
attribute_1:
|
| 14 |
+
- material_8
|
| 15 |
+
- material_8
|
| 16 |
+
- material_6
|
| 17 |
+
attribute_2:
|
| 18 |
+
- 5
|
| 19 |
+
- 5
|
| 20 |
+
- 6
|
| 21 |
+
attribute_3:
|
| 22 |
+
- 8
|
| 23 |
+
- 8
|
| 24 |
+
- 9
|
| 25 |
+
loading:
|
| 26 |
+
- 154.02
|
| 27 |
+
- 108.73
|
| 28 |
+
- 99.84
|
| 29 |
+
measurement_0:
|
| 30 |
+
- 14
|
| 31 |
+
- 4
|
| 32 |
+
- 6
|
| 33 |
+
measurement_1:
|
| 34 |
+
- 6
|
| 35 |
+
- 7
|
| 36 |
+
- 7
|
| 37 |
+
measurement_10:
|
| 38 |
+
- 16.637
|
| 39 |
+
- 16.207
|
| 40 |
+
- 17.17
|
| 41 |
+
measurement_11:
|
| 42 |
+
- 20.719
|
| 43 |
+
- 20.058
|
| 44 |
+
- 20.858
|
| 45 |
+
measurement_12:
|
| 46 |
+
- 12.824
|
| 47 |
+
- 11.898
|
| 48 |
+
- 10.968
|
| 49 |
+
measurement_13:
|
| 50 |
+
- 16.067
|
| 51 |
+
- 13.871
|
| 52 |
+
- 16.448
|
| 53 |
+
measurement_14:
|
| 54 |
+
- 15.181
|
| 55 |
+
- 14.266
|
| 56 |
+
- 15.6
|
| 57 |
+
measurement_15:
|
| 58 |
+
- 18.546
|
| 59 |
+
- 15.734
|
| 60 |
+
- 14.637
|
| 61 |
+
measurement_16:
|
| 62 |
+
- 19.402
|
| 63 |
+
- 16.886
|
| 64 |
+
- 13.86
|
| 65 |
+
measurement_17:
|
| 66 |
+
- 643.086
|
| 67 |
+
- 642.533
|
| 68 |
+
- 673.545
|
| 69 |
+
measurement_2:
|
| 70 |
+
- 6
|
| 71 |
+
- 9
|
| 72 |
+
- 6
|
| 73 |
+
measurement_3:
|
| 74 |
+
- 19.532
|
| 75 |
+
- 18.128
|
| 76 |
+
- NaN
|
| 77 |
+
measurement_4:
|
| 78 |
+
- 11.017
|
| 79 |
+
- 11.866
|
| 80 |
+
- 10.064
|
| 81 |
+
measurement_5:
|
| 82 |
+
- 15.639
|
| 83 |
+
- 17.891
|
| 84 |
+
- 16.287
|
| 85 |
+
measurement_6:
|
| 86 |
+
- 16.709
|
| 87 |
+
- 20.302
|
| 88 |
+
- 17.445
|
| 89 |
+
measurement_7:
|
| 90 |
+
- 10.057
|
| 91 |
+
- NaN
|
| 92 |
+
- 12.117
|
| 93 |
+
measurement_8:
|
| 94 |
+
- 20.201
|
| 95 |
+
- 18.148
|
| 96 |
+
- 20.659
|
| 97 |
+
measurement_9:
|
| 98 |
+
- 11.106
|
| 99 |
+
- 10.221
|
| 100 |
+
- 11.999
|
| 101 |
+
product_code:
|
| 102 |
+
- C
|
| 103 |
+
- C
|
| 104 |
+
- E
|
| 105 |
+
---
|
| 106 |
+
|
| 107 |
+
# Model description
|
| 108 |
+
|
| 109 |
+
This is a DecisionTreeClassifier model built for Kaggle Tabular Playground Series August 2022, trained on supersoaker production failures dataset.
|
| 110 |
+
|
| 111 |
+
## Intended uses & limitations
|
| 112 |
+
|
| 113 |
+
This model is not ready to be used in production.
|
| 114 |
+
|
| 115 |
+
## Training Procedure
|
| 116 |
+
|
| 117 |
+
### Hyperparameters
|
| 118 |
+
|
| 119 |
+
The model is trained with below hyperparameters.
|
| 120 |
+
|
| 121 |
+
<details>
|
| 122 |
+
<summary> Click to expand </summary>
|
| 123 |
+
|
| 124 |
+
| Hyperparameter | Value |
|
| 125 |
+
|-----------------------------------------------------------------|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
|
| 126 |
+
| memory | |
|
| 127 |
+
| steps | [('transformation', ColumnTransformer(transformers=[('loading_missing_value_imputer',
|
| 128 |
+
SimpleImputer(), ['loading']),
|
| 129 |
+
('numerical_missing_value_imputer',
|
| 130 |
+
SimpleImputer(),
|
| 131 |
+
['loading', 'measurement_3', 'measurement_4',
|
| 132 |
+
'measurement_5', 'measurement_6',
|
| 133 |
+
'measurement_7', 'measurement_8',
|
| 134 |
+
'measurement_9', 'measurement_10',
|
| 135 |
+
'measurement_11', 'measurement_12',
|
| 136 |
+
'measurement_13', 'measurement_14',
|
| 137 |
+
'measurement_15', 'measurement_16',
|
| 138 |
+
'measurement_17']),
|
| 139 |
+
('attribute_0_encoder', OneHotEncoder(),
|
| 140 |
+
['attribute_0']),
|
| 141 |
+
('attribute_1_encoder', OneHotEncoder(),
|
| 142 |
+
['attribute_1']),
|
| 143 |
+
('product_code_encoder', OneHotEncoder(),
|
| 144 |
+
['product_code'])])), ('model', DecisionTreeClassifier(max_depth=4))] |
|
| 145 |
+
| verbose | False |
|
| 146 |
+
| transformation | ColumnTransformer(transformers=[('loading_missing_value_imputer',
|
| 147 |
+
SimpleImputer(), ['loading']),
|
| 148 |
+
('numerical_missing_value_imputer',
|
| 149 |
+
SimpleImputer(),
|
| 150 |
+
['loading', 'measurement_3', 'measurement_4',
|
| 151 |
+
'measurement_5', 'measurement_6',
|
| 152 |
+
'measurement_7', 'measurement_8',
|
| 153 |
+
'measurement_9', 'measurement_10',
|
| 154 |
+
'measurement_11', 'measurement_12',
|
| 155 |
+
'measurement_13', 'measurement_14',
|
| 156 |
+
'measurement_15', 'measurement_16',
|
| 157 |
+
'measurement_17']),
|
| 158 |
+
('attribute_0_encoder', OneHotEncoder(),
|
| 159 |
+
['attribute_0']),
|
| 160 |
+
('attribute_1_encoder', OneHotEncoder(),
|
| 161 |
+
['attribute_1']),
|
| 162 |
+
('product_code_encoder', OneHotEncoder(),
|
| 163 |
+
['product_code'])]) |
|
| 164 |
+
| model | DecisionTreeClassifier(max_depth=4) |
|
| 165 |
+
| transformation__n_jobs | |
|
| 166 |
+
| transformation__remainder | drop |
|
| 167 |
+
| transformation__sparse_threshold | 0.3 |
|
| 168 |
+
| transformation__transformer_weights | |
|
| 169 |
+
| transformation__transformers | [('loading_missing_value_imputer', SimpleImputer(), ['loading']), ('numerical_missing_value_imputer', SimpleImputer(), ['loading', 'measurement_3', 'measurement_4', 'measurement_5', 'measurement_6', 'measurement_7', 'measurement_8', 'measurement_9', 'measurement_10', 'measurement_11', 'measurement_12', 'measurement_13', 'measurement_14', 'measurement_15', 'measurement_16', 'measurement_17']), ('attribute_0_encoder', OneHotEncoder(), ['attribute_0']), ('attribute_1_encoder', OneHotEncoder(), ['attribute_1']), ('product_code_encoder', OneHotEncoder(), ['product_code'])] |
|
| 170 |
+
| transformation__verbose | False |
|
| 171 |
+
| transformation__verbose_feature_names_out | True |
|
| 172 |
+
| transformation__loading_missing_value_imputer | SimpleImputer() |
|
| 173 |
+
| transformation__numerical_missing_value_imputer | SimpleImputer() |
|
| 174 |
+
| transformation__attribute_0_encoder | OneHotEncoder() |
|
| 175 |
+
| transformation__attribute_1_encoder | OneHotEncoder() |
|
| 176 |
+
| transformation__product_code_encoder | OneHotEncoder() |
|
| 177 |
+
| transformation__loading_missing_value_imputer__add_indicator | False |
|
| 178 |
+
| transformation__loading_missing_value_imputer__copy | True |
|
| 179 |
+
| transformation__loading_missing_value_imputer__fill_value | |
|
| 180 |
+
| transformation__loading_missing_value_imputer__missing_values | nan |
|
| 181 |
+
| transformation__loading_missing_value_imputer__strategy | mean |
|
| 182 |
+
| transformation__loading_missing_value_imputer__verbose | 0 |
|
| 183 |
+
| transformation__numerical_missing_value_imputer__add_indicator | False |
|
| 184 |
+
| transformation__numerical_missing_value_imputer__copy | True |
|
| 185 |
+
| transformation__numerical_missing_value_imputer__fill_value | |
|
| 186 |
+
| transformation__numerical_missing_value_imputer__missing_values | nan |
|
| 187 |
+
| transformation__numerical_missing_value_imputer__strategy | mean |
|
| 188 |
+
| transformation__numerical_missing_value_imputer__verbose | 0 |
|
| 189 |
+
| transformation__attribute_0_encoder__categories | auto |
|
| 190 |
+
| transformation__attribute_0_encoder__drop | |
|
| 191 |
+
| transformation__attribute_0_encoder__dtype | <class 'numpy.float64'> |
|
| 192 |
+
| transformation__attribute_0_encoder__handle_unknown | error |
|
| 193 |
+
| transformation__attribute_0_encoder__sparse | True |
|
| 194 |
+
| transformation__attribute_1_encoder__categories | auto |
|
| 195 |
+
| transformation__attribute_1_encoder__drop | |
|
| 196 |
+
| transformation__attribute_1_encoder__dtype | <class 'numpy.float64'> |
|
| 197 |
+
| transformation__attribute_1_encoder__handle_unknown | error |
|
| 198 |
+
| transformation__attribute_1_encoder__sparse | True |
|
| 199 |
+
| transformation__product_code_encoder__categories | auto |
|
| 200 |
+
| transformation__product_code_encoder__drop | |
|
| 201 |
+
| transformation__product_code_encoder__dtype | <class 'numpy.float64'> |
|
| 202 |
+
| transformation__product_code_encoder__handle_unknown | error |
|
| 203 |
+
| transformation__product_code_encoder__sparse | True |
|
| 204 |
+
| model__ccp_alpha | 0.0 |
|
| 205 |
+
| model__class_weight | |
|
| 206 |
+
| model__criterion | gini |
|
| 207 |
+
| model__max_depth | 4 |
|
| 208 |
+
| model__max_features | |
|
| 209 |
+
| model__max_leaf_nodes | |
|
| 210 |
+
| model__min_impurity_decrease | 0.0 |
|
| 211 |
+
| model__min_samples_leaf | 1 |
|
| 212 |
+
| model__min_samples_split | 2 |
|
| 213 |
+
| model__min_weight_fraction_leaf | 0.0 |
|
| 214 |
+
| model__random_state | |
|
| 215 |
+
| model__splitter | best |
|
| 216 |
+
|
| 217 |
+
</details>
|
| 218 |
+
|
| 219 |
+
### Model Plot
|
| 220 |
+
|
| 221 |
+
The model plot is below.
|
| 222 |
+
|
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<style>#sk-b8914d13-cacb-404b-89fd-48f0ed8d671f {color: black;background-color: white;}#sk-b8914d13-cacb-404b-89fd-48f0ed8d671f pre{padding: 0;}#sk-b8914d13-cacb-404b-89fd-48f0ed8d671f div.sk-toggleable {background-color: white;}#sk-b8914d13-cacb-404b-89fd-48f0ed8d671f label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-b8914d13-cacb-404b-89fd-48f0ed8d671f label.sk-toggleable__label-arrow:before {content: "▸";float: left;margin-right: 0.25em;color: #696969;}#sk-b8914d13-cacb-404b-89fd-48f0ed8d671f label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-b8914d13-cacb-404b-89fd-48f0ed8d671f div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-b8914d13-cacb-404b-89fd-48f0ed8d671f div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-b8914d13-cacb-404b-89fd-48f0ed8d671f div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-b8914d13-cacb-404b-89fd-48f0ed8d671f input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-b8914d13-cacb-404b-89fd-48f0ed8d671f input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: "▾";}#sk-b8914d13-cacb-404b-89fd-48f0ed8d671f div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-b8914d13-cacb-404b-89fd-48f0ed8d671f div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-b8914d13-cacb-404b-89fd-48f0ed8d671f input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-b8914d13-cacb-404b-89fd-48f0ed8d671f div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-b8914d13-cacb-404b-89fd-48f0ed8d671f div.sk-estimator:hover {background-color: #d4ebff;}#sk-b8914d13-cacb-404b-89fd-48f0ed8d671f div.sk-parallel-item::after {content: "";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-b8914d13-cacb-404b-89fd-48f0ed8d671f div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-b8914d13-cacb-404b-89fd-48f0ed8d671f div.sk-serial::before {content: "";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 2em;bottom: 0;left: 50%;}#sk-b8914d13-cacb-404b-89fd-48f0ed8d671f div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;}#sk-b8914d13-cacb-404b-89fd-48f0ed8d671f div.sk-item {z-index: 1;}#sk-b8914d13-cacb-404b-89fd-48f0ed8d671f div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;}#sk-b8914d13-cacb-404b-89fd-48f0ed8d671f div.sk-parallel::before {content: "";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 2em;bottom: 0;left: 50%;}#sk-b8914d13-cacb-404b-89fd-48f0ed8d671f div.sk-parallel-item {display: flex;flex-direction: column;position: relative;background-color: white;}#sk-b8914d13-cacb-404b-89fd-48f0ed8d671f div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-b8914d13-cacb-404b-89fd-48f0ed8d671f div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-b8914d13-cacb-404b-89fd-48f0ed8d671f div.sk-parallel-item:only-child::after {width: 0;}#sk-b8914d13-cacb-404b-89fd-48f0ed8d671f div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;position: relative;}#sk-b8914d13-cacb-404b-89fd-48f0ed8d671f div.sk-label label {font-family: monospace;font-weight: bold;background-color: white;display: inline-block;line-height: 1.2em;}#sk-b8914d13-cacb-404b-89fd-48f0ed8d671f div.sk-label-container {position: relative;z-index: 2;text-align: center;}#sk-b8914d13-cacb-404b-89fd-48f0ed8d671f div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-b8914d13-cacb-404b-89fd-48f0ed8d671f div.sk-text-repr-fallback {display: none;}</style><div id="sk-b8914d13-cacb-404b-89fd-48f0ed8d671f" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>Pipeline(steps=[('transformation',ColumnTransformer(transformers=[('loading_missing_value_imputer',SimpleImputer(),['loading']),('numerical_missing_value_imputer',SimpleImputer(),['loading', 'measurement_3','measurement_4','measurement_5','measurement_6','measurement_7','measurement_8','measurement_9','measurement_10','measurement_11','measurement_12','measurement_13','measurement_14','measurement_15','measurement_16','measurement_17']),('attribute_0_encoder',OneHotEncoder(),['attribute_0']),('attribute_1_encoder',OneHotEncoder(),['attribute_1']),('product_code_encoder',OneHotEncoder(),['product_code'])])),('model', DecisionTreeClassifier(max_depth=4))])</pre><b>Please rerun this cell to show the HTML repr or trust the notebook.</b></div><div class="sk-container" hidden><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="fe201304-214c-493b-8896-11cea0894f6e" type="checkbox" ><label for="fe201304-214c-493b-8896-11cea0894f6e" class="sk-toggleable__label sk-toggleable__label-arrow">Pipeline</label><div class="sk-toggleable__content"><pre>Pipeline(steps=[('transformation',ColumnTransformer(transformers=[('loading_missing_value_imputer',SimpleImputer(),['loading']),('numerical_missing_value_imputer',SimpleImputer(),['loading', 'measurement_3','measurement_4','measurement_5','measurement_6','measurement_7','measurement_8','measurement_9','measurement_10','measurement_11','measurement_12','measurement_13','measurement_14','measurement_15','measurement_16','measurement_17']),('attribute_0_encoder',OneHotEncoder(),['attribute_0']),('attribute_1_encoder',OneHotEncoder(),['attribute_1']),('product_code_encoder',OneHotEncoder(),['product_code'])])),('model', DecisionTreeClassifier(max_depth=4))])</pre></div></div></div><div class="sk-serial"><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="19136b49-925c-40a2-b4d1-37039bb014a9" type="checkbox" ><label for="19136b49-925c-40a2-b4d1-37039bb014a9" class="sk-toggleable__label sk-toggleable__label-arrow">transformation: ColumnTransformer</label><div class="sk-toggleable__content"><pre>ColumnTransformer(transformers=[('loading_missing_value_imputer',SimpleImputer(), ['loading']),('numerical_missing_value_imputer',SimpleImputer(),['loading', 'measurement_3', 'measurement_4','measurement_5', 'measurement_6','measurement_7', 'measurement_8','measurement_9', 'measurement_10','measurement_11', 'measurement_12','measurement_13', 'measurement_14','measurement_15', 'measurement_16','measurement_17']),('attribute_0_encoder', OneHotEncoder(),['attribute_0']),('attribute_1_encoder', OneHotEncoder(),['attribute_1']),('product_code_encoder', OneHotEncoder(),['product_code'])])</pre></div></div></div><div class="sk-parallel"><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="c8ec7f92-b10a-41e7-b673-1239572ea00e" type="checkbox" ><label for="c8ec7f92-b10a-41e7-b673-1239572ea00e" class="sk-toggleable__label sk-toggleable__label-arrow">loading_missing_value_imputer</label><div class="sk-toggleable__content"><pre>['loading']</pre></div></div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="70fec50e-9c49-4818-a58f-ef8de932035c" type="checkbox" ><label for="70fec50e-9c49-4818-a58f-ef8de932035c" class="sk-toggleable__label sk-toggleable__label-arrow">SimpleImputer</label><div class="sk-toggleable__content"><pre>SimpleImputer()</pre></div></div></div></div></div></div><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="ac8a6641-4222-4b12-b691-928201d9af73" type="checkbox" ><label for="ac8a6641-4222-4b12-b691-928201d9af73" class="sk-toggleable__label sk-toggleable__label-arrow">numerical_missing_value_imputer</label><div class="sk-toggleable__content"><pre>['loading', 'measurement_3', 'measurement_4', 'measurement_5', 'measurement_6', 'measurement_7', 'measurement_8', 'measurement_9', 'measurement_10', 'measurement_11', 'measurement_12', 'measurement_13', 'measurement_14', 'measurement_15', 'measurement_16', 'measurement_17']</pre></div></div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="a14b63c1-fecb-445e-9a74-8229a531f0ea" type="checkbox" ><label for="a14b63c1-fecb-445e-9a74-8229a531f0ea" class="sk-toggleable__label sk-toggleable__label-arrow">SimpleImputer</label><div class="sk-toggleable__content"><pre>SimpleImputer()</pre></div></div></div></div></div></div><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="80227cfc-e001-4c0d-b495-e4e0631a49d5" type="checkbox" ><label for="80227cfc-e001-4c0d-b495-e4e0631a49d5" class="sk-toggleable__label sk-toggleable__label-arrow">attribute_0_encoder</label><div class="sk-toggleable__content"><pre>['attribute_0']</pre></div></div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="c52efc0c-08b7-467a-a0a1-f07cb6cecebc" type="checkbox" ><label for="c52efc0c-08b7-467a-a0a1-f07cb6cecebc" class="sk-toggleable__label sk-toggleable__label-arrow">OneHotEncoder</label><div class="sk-toggleable__content"><pre>OneHotEncoder()</pre></div></div></div></div></div></div><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="6da0ab07-3d41-459c-a8a6-a56960b775f2" type="checkbox" ><label for="6da0ab07-3d41-459c-a8a6-a56960b775f2" class="sk-toggleable__label sk-toggleable__label-arrow">attribute_1_encoder</label><div class="sk-toggleable__content"><pre>['attribute_1']</pre></div></div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="b515fbe5-466a-4eb7-84d9-35227a1e862a" type="checkbox" ><label for="b515fbe5-466a-4eb7-84d9-35227a1e862a" class="sk-toggleable__label sk-toggleable__label-arrow">OneHotEncoder</label><div class="sk-toggleable__content"><pre>OneHotEncoder()</pre></div></div></div></div></div></div><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="72c4b8e6-3110-486f-8b33-a7db1f5e822f" type="checkbox" ><label for="72c4b8e6-3110-486f-8b33-a7db1f5e822f" class="sk-toggleable__label sk-toggleable__label-arrow">product_code_encoder</label><div class="sk-toggleable__content"><pre>['product_code']</pre></div></div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="f3bfb5a1-317d-4ff4-8dd0-804ef1d7fd61" type="checkbox" ><label for="f3bfb5a1-317d-4ff4-8dd0-804ef1d7fd61" class="sk-toggleable__label sk-toggleable__label-arrow">OneHotEncoder</label><div class="sk-toggleable__content"><pre>OneHotEncoder()</pre></div></div></div></div></div></div></div></div><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="dbcb65f9-3068-4263-9c1c-2e6413804681" type="checkbox" ><label for="dbcb65f9-3068-4263-9c1c-2e6413804681" class="sk-toggleable__label sk-toggleable__label-arrow">DecisionTreeClassifier</label><div class="sk-toggleable__content"><pre>DecisionTreeClassifier(max_depth=4)</pre></div></div></div></div></div></div></div>
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## Evaluation Results
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You can find the details about evaluation process and the evaluation results.
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| Metric | Value |
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|----------|---------|
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| accuracy | 0.7888 |
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| f1 score | 0.7888 |
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# How to Get Started with the Model
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Use the code below to get started with the model.
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<details>
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<summary> Click to expand </summary>
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```python
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import pickle
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with open(decision-tree-playground-kaggle/model.pkl, 'rb') as file:
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clf = pickle.load(file)
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```
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</details>
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# Model Card Authors
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This model card is written by following authors:
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huggingface
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# Model Card Contact
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You can contact the model card authors through following channels:
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[More Information Needed]
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# Citation
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Below you can find information related to citation.
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**BibTeX:**
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```
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[More Information Needed]
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```
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Tree Plot
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Confusion Matrix
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