pushing files to the repo from the example!
Browse files- README.md +281 -0
- config.json +159 -0
- confusion_matrix.png +0 -0
- model.pkl +3 -0
- tree.png +0 -0
README.md
ADDED
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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_6
|
| 16 |
+
- material_8
|
| 17 |
+
attribute_2:
|
| 18 |
+
- 9
|
| 19 |
+
- 6
|
| 20 |
+
- 5
|
| 21 |
+
attribute_3:
|
| 22 |
+
- 5
|
| 23 |
+
- 9
|
| 24 |
+
- 8
|
| 25 |
+
loading:
|
| 26 |
+
- 119.49
|
| 27 |
+
- 85.36
|
| 28 |
+
- 73.71
|
| 29 |
+
measurement_0:
|
| 30 |
+
- 11
|
| 31 |
+
- 10
|
| 32 |
+
- 24
|
| 33 |
+
measurement_1:
|
| 34 |
+
- 2
|
| 35 |
+
- 8
|
| 36 |
+
- 7
|
| 37 |
+
measurement_10:
|
| 38 |
+
- 17.138
|
| 39 |
+
- 15.632
|
| 40 |
+
- 15.854
|
| 41 |
+
measurement_11:
|
| 42 |
+
- 19.954
|
| 43 |
+
- 18.992
|
| 44 |
+
- 20.405
|
| 45 |
+
measurement_12:
|
| 46 |
+
- 12.348
|
| 47 |
+
- .nan
|
| 48 |
+
- 13.638
|
| 49 |
+
measurement_13:
|
| 50 |
+
- 13.93
|
| 51 |
+
- 15.148
|
| 52 |
+
- .nan
|
| 53 |
+
measurement_14:
|
| 54 |
+
- 15.889
|
| 55 |
+
- .nan
|
| 56 |
+
- 15.854
|
| 57 |
+
measurement_15:
|
| 58 |
+
- 15.831
|
| 59 |
+
- 15.849
|
| 60 |
+
- 16.555
|
| 61 |
+
measurement_16:
|
| 62 |
+
- 16.102
|
| 63 |
+
- 15.896
|
| 64 |
+
- 17.145
|
| 65 |
+
measurement_17:
|
| 66 |
+
- 643.509
|
| 67 |
+
- 722.585
|
| 68 |
+
- 802.57
|
| 69 |
+
measurement_2:
|
| 70 |
+
- 3
|
| 71 |
+
- 3
|
| 72 |
+
- 7
|
| 73 |
+
measurement_3:
|
| 74 |
+
- 17.659
|
| 75 |
+
- 19.679
|
| 76 |
+
- 17.291
|
| 77 |
+
measurement_4:
|
| 78 |
+
- 11.578
|
| 79 |
+
- 11.49
|
| 80 |
+
- 11.691
|
| 81 |
+
measurement_5:
|
| 82 |
+
- 15.514
|
| 83 |
+
- 18.267
|
| 84 |
+
- 18.289
|
| 85 |
+
measurement_6:
|
| 86 |
+
- 15.99
|
| 87 |
+
- 17.921
|
| 88 |
+
- 17.396
|
| 89 |
+
measurement_7:
|
| 90 |
+
- 12.231
|
| 91 |
+
- 11.978
|
| 92 |
+
- 11.361
|
| 93 |
+
measurement_8:
|
| 94 |
+
- 19.92
|
| 95 |
+
- 18.135
|
| 96 |
+
- 19.67
|
| 97 |
+
measurement_9:
|
| 98 |
+
- 10.555
|
| 99 |
+
- 11.113
|
| 100 |
+
- 11.375
|
| 101 |
+
product_code:
|
| 102 |
+
- A
|
| 103 |
+
- E
|
| 104 |
+
- C
|
| 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 |
+
|
| 223 |
+
<style>#sk-b89bd1e9-e872-49aa-bc53-73dc52fc4e14 {color: black;background-color: white;}#sk-b89bd1e9-e872-49aa-bc53-73dc52fc4e14 pre{padding: 0;}#sk-b89bd1e9-e872-49aa-bc53-73dc52fc4e14 div.sk-toggleable {background-color: white;}#sk-b89bd1e9-e872-49aa-bc53-73dc52fc4e14 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-b89bd1e9-e872-49aa-bc53-73dc52fc4e14 label.sk-toggleable__label-arrow:before {content: "▸";float: left;margin-right: 0.25em;color: #696969;}#sk-b89bd1e9-e872-49aa-bc53-73dc52fc4e14 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-b89bd1e9-e872-49aa-bc53-73dc52fc4e14 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-b89bd1e9-e872-49aa-bc53-73dc52fc4e14 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-b89bd1e9-e872-49aa-bc53-73dc52fc4e14 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-b89bd1e9-e872-49aa-bc53-73dc52fc4e14 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-b89bd1e9-e872-49aa-bc53-73dc52fc4e14 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: "▾";}#sk-b89bd1e9-e872-49aa-bc53-73dc52fc4e14 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-b89bd1e9-e872-49aa-bc53-73dc52fc4e14 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-b89bd1e9-e872-49aa-bc53-73dc52fc4e14 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-b89bd1e9-e872-49aa-bc53-73dc52fc4e14 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-b89bd1e9-e872-49aa-bc53-73dc52fc4e14 div.sk-estimator:hover {background-color: #d4ebff;}#sk-b89bd1e9-e872-49aa-bc53-73dc52fc4e14 div.sk-parallel-item::after {content: "";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-b89bd1e9-e872-49aa-bc53-73dc52fc4e14 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-b89bd1e9-e872-49aa-bc53-73dc52fc4e14 div.sk-serial::before {content: "";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 2em;bottom: 0;left: 50%;}#sk-b89bd1e9-e872-49aa-bc53-73dc52fc4e14 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;}#sk-b89bd1e9-e872-49aa-bc53-73dc52fc4e14 div.sk-item {z-index: 1;}#sk-b89bd1e9-e872-49aa-bc53-73dc52fc4e14 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;}#sk-b89bd1e9-e872-49aa-bc53-73dc52fc4e14 div.sk-parallel::before {content: "";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 2em;bottom: 0;left: 50%;}#sk-b89bd1e9-e872-49aa-bc53-73dc52fc4e14 div.sk-parallel-item {display: flex;flex-direction: column;position: relative;background-color: white;}#sk-b89bd1e9-e872-49aa-bc53-73dc52fc4e14 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-b89bd1e9-e872-49aa-bc53-73dc52fc4e14 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-b89bd1e9-e872-49aa-bc53-73dc52fc4e14 div.sk-parallel-item:only-child::after {width: 0;}#sk-b89bd1e9-e872-49aa-bc53-73dc52fc4e14 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-b89bd1e9-e872-49aa-bc53-73dc52fc4e14 div.sk-label label {font-family: monospace;font-weight: bold;background-color: white;display: inline-block;line-height: 1.2em;}#sk-b89bd1e9-e872-49aa-bc53-73dc52fc4e14 div.sk-label-container {position: relative;z-index: 2;text-align: center;}#sk-b89bd1e9-e872-49aa-bc53-73dc52fc4e14 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-b89bd1e9-e872-49aa-bc53-73dc52fc4e14 div.sk-text-repr-fallback {display: none;}</style><div id="sk-b89bd1e9-e872-49aa-bc53-73dc52fc4e14" 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="82f19dd0-da3e-499c-84b9-f67ed489906d" type="checkbox" ><label for="82f19dd0-da3e-499c-84b9-f67ed489906d" 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="e3bc6996-eefc-4601-a7df-7850743b36d6" type="checkbox" ><label for="e3bc6996-eefc-4601-a7df-7850743b36d6" 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="9cc3e3ef-17e8-4bf4-a121-0a29a377373e" type="checkbox" ><label for="9cc3e3ef-17e8-4bf4-a121-0a29a377373e" 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="e7ba5c2d-3bf3-444f-be00-0ecf4b563cd7" type="checkbox" ><label for="e7ba5c2d-3bf3-444f-be00-0ecf4b563cd7" 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="2277368d-30f2-46c1-a283-9f0ccf350872" type="checkbox" ><label for="2277368d-30f2-46c1-a283-9f0ccf350872" 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="2a49159e-c23f-4cbe-92bb-09bb64c1354d" type="checkbox" ><label for="2a49159e-c23f-4cbe-92bb-09bb64c1354d" 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="c87d52bb-0b23-4e43-abe8-afc3759dac02" type="checkbox" ><label for="c87d52bb-0b23-4e43-abe8-afc3759dac02" 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="023971df-ed99-4eaf-8f0d-cd115bacbb45" type="checkbox" ><label for="023971df-ed99-4eaf-8f0d-cd115bacbb45" 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="111f5303-3f63-409a-9dc1-74ab94419974" type="checkbox" ><label for="111f5303-3f63-409a-9dc1-74ab94419974" 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="c858e1b1-b68f-4700-9111-32772a7b51ab" type="checkbox" ><label for="c858e1b1-b68f-4700-9111-32772a7b51ab" 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="5ce65801-d4be-48d4-81d3-7998e483cf65" type="checkbox" ><label for="5ce65801-d4be-48d4-81d3-7998e483cf65" 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="75a1cce7-c7f7-41cf-bb4d-8e403291a41b" type="checkbox" ><label for="75a1cce7-c7f7-41cf-bb4d-8e403291a41b" 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="3c311565-4080-492c-b353-fbc41e1c17d5" type="checkbox" ><label for="3c311565-4080-492c-b353-fbc41e1c17d5" 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 | 0.786392 |
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| f1 score | 0.786392 |
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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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+
|
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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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+
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# Model Card Authors
|
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+
This model card is written by following authors:
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+
|
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huggingface
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+
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+
# Model Card Contact
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+
|
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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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+
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+
# Citation
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+
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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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+
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Confusion Matrix
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|
config.json
ADDED
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|
| 1 |
+
{
|
| 2 |
+
"sklearn": {
|
| 3 |
+
"columns": [
|
| 4 |
+
"product_code",
|
| 5 |
+
"loading",
|
| 6 |
+
"attribute_0",
|
| 7 |
+
"attribute_1",
|
| 8 |
+
"attribute_2",
|
| 9 |
+
"attribute_3",
|
| 10 |
+
"measurement_0",
|
| 11 |
+
"measurement_1",
|
| 12 |
+
"measurement_2",
|
| 13 |
+
"measurement_3",
|
| 14 |
+
"measurement_4",
|
| 15 |
+
"measurement_5",
|
| 16 |
+
"measurement_6",
|
| 17 |
+
"measurement_7",
|
| 18 |
+
"measurement_8",
|
| 19 |
+
"measurement_9",
|
| 20 |
+
"measurement_10",
|
| 21 |
+
"measurement_11",
|
| 22 |
+
"measurement_12",
|
| 23 |
+
"measurement_13",
|
| 24 |
+
"measurement_14",
|
| 25 |
+
"measurement_15",
|
| 26 |
+
"measurement_16",
|
| 27 |
+
"measurement_17"
|
| 28 |
+
],
|
| 29 |
+
"environment": [
|
| 30 |
+
"scikit-learn=1.0.2"
|
| 31 |
+
],
|
| 32 |
+
"example_input": {
|
| 33 |
+
"attribute_0": [
|
| 34 |
+
"material_7",
|
| 35 |
+
"material_7",
|
| 36 |
+
"material_7"
|
| 37 |
+
],
|
| 38 |
+
"attribute_1": [
|
| 39 |
+
"material_8",
|
| 40 |
+
"material_6",
|
| 41 |
+
"material_8"
|
| 42 |
+
],
|
| 43 |
+
"attribute_2": [
|
| 44 |
+
9,
|
| 45 |
+
6,
|
| 46 |
+
5
|
| 47 |
+
],
|
| 48 |
+
"attribute_3": [
|
| 49 |
+
5,
|
| 50 |
+
9,
|
| 51 |
+
8
|
| 52 |
+
],
|
| 53 |
+
"loading": [
|
| 54 |
+
119.49,
|
| 55 |
+
85.36,
|
| 56 |
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| 57 |
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| 58 |
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|
| 59 |
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|
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| 61 |
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|
| 62 |
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|
| 63 |
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|
| 64 |
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|
| 65 |
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|
| 66 |
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|
| 67 |
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| 68 |
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|
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|
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| 72 |
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| 73 |
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|
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20.405
|
| 77 |
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|
| 78 |
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|
| 79 |
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|
| 80 |
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| 81 |
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13.638
|
| 82 |
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| 83 |
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|
| 84 |
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13.93,
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|
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|
| 87 |
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|
| 88 |
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|
| 89 |
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|
| 90 |
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| 91 |
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|
| 92 |
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],
|
| 93 |
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| 94 |
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| 101 |
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|
| 102 |
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|
| 103 |
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"measurement_17": [
|
| 104 |
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643.509,
|
| 105 |
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722.585,
|
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802.57
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| 107 |
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],
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| 108 |
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|
| 109 |
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| 110 |
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3,
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| 111 |
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7
|
| 112 |
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|
| 113 |
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|
| 114 |
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| 118 |
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| 119 |
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| 123 |
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|
| 124 |
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15.514,
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|
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],
|
| 128 |
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| 129 |
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|
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17.921,
|
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17.396
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| 132 |
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|
| 133 |
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|
| 134 |
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|
| 136 |
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11.361
|
| 137 |
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|
| 138 |
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|
| 139 |
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|
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|
| 141 |
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19.67
|
| 142 |
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|
| 143 |
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|
| 144 |
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10.555,
|
| 145 |
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|
| 146 |
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11.375
|
| 147 |
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|
| 148 |
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"product_code": [
|
| 149 |
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"A",
|
| 150 |
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"E",
|
| 151 |
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"C"
|
| 152 |
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]
|
| 153 |
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},
|
| 154 |
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"model": {
|
| 155 |
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"file": "model.pkl"
|
| 156 |
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},
|
| 157 |
+
"task": "tabular-classification"
|
| 158 |
+
}
|
| 159 |
+
}
|
confusion_matrix.png
ADDED
|
model.pkl
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
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|
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|
| 1 |
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version https://git-lfs.github.com/spec/v1
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| 2 |
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oid sha256:0b657336bbde87de9beb4cffee3e500e48379fca8417bcdf538ceea6e6d59bb9
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| 3 |
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size 6824
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tree.png
ADDED
|