File size: 56,509 Bytes
59a7363 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 |
---
library_name: sklearn
tags:
- sklearn
- skops
- tabular-classification
widget:
structuredData:
attribute_0:
- material_7
- material_7
- material_7
attribute_1:
- material_8
- material_8
- material_6
attribute_2:
- 5
- 5
- 6
attribute_3:
- 8
- 8
- 9
loading:
- 154.02
- 108.73
- 99.84
measurement_0:
- 14
- 4
- 6
measurement_1:
- 6
- 7
- 7
measurement_10:
- 16.637
- 16.207
- 17.17
measurement_11:
- 20.719
- 20.058
- 20.858
measurement_12:
- 12.824
- 11.898
- 10.968
measurement_13:
- 16.067
- 13.871
- 16.448
measurement_14:
- 15.181
- 14.266
- 15.6
measurement_15:
- 18.546
- 15.734
- 14.637
measurement_16:
- 19.402
- 16.886
- 13.86
measurement_17:
- 643.086
- 642.533
- 673.545
measurement_2:
- 6
- 9
- 6
measurement_3:
- 19.532
- 18.128
- NaN
measurement_4:
- 11.017
- 11.866
- 10.064
measurement_5:
- 15.639
- 17.891
- 16.287
measurement_6:
- 16.709
- 20.302
- 17.445
measurement_7:
- 10.057
- NaN
- 12.117
measurement_8:
- 20.201
- 18.148
- 20.659
measurement_9:
- 11.106
- 10.221
- 11.999
product_code:
- C
- C
- E
---
# Model description
This is a DecisionTreeClassifier model built for Kaggle Tabular Playground Series August 2022, trained on supersoaker production failures dataset.
## Intended uses & limitations
This model is not ready to be used in production.
## Training Procedure
### Hyperparameters
The model is trained with below hyperparameters.
<details>
<summary> Click to expand </summary>
| Hyperparameter | Value |
|-----------------------------------------------------------------|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| memory | |
| 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))] |
| verbose | False |
| 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) |
| transformation__n_jobs | |
| transformation__remainder | drop |
| transformation__sparse_threshold | 0.3 |
| transformation__transformer_weights | |
| 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'])] |
| transformation__verbose | False |
| transformation__verbose_feature_names_out | True |
| transformation__loading_missing_value_imputer | SimpleImputer() |
| transformation__numerical_missing_value_imputer | SimpleImputer() |
| transformation__attribute_0_encoder | OneHotEncoder() |
| transformation__attribute_1_encoder | OneHotEncoder() |
| transformation__product_code_encoder | OneHotEncoder() |
| transformation__loading_missing_value_imputer__add_indicator | False |
| transformation__loading_missing_value_imputer__copy | True |
| transformation__loading_missing_value_imputer__fill_value | |
| transformation__loading_missing_value_imputer__missing_values | nan |
| transformation__loading_missing_value_imputer__strategy | mean |
| transformation__loading_missing_value_imputer__verbose | 0 |
| transformation__numerical_missing_value_imputer__add_indicator | False |
| transformation__numerical_missing_value_imputer__copy | True |
| transformation__numerical_missing_value_imputer__fill_value | |
| transformation__numerical_missing_value_imputer__missing_values | nan |
| transformation__numerical_missing_value_imputer__strategy | mean |
| transformation__numerical_missing_value_imputer__verbose | 0 |
| transformation__attribute_0_encoder__categories | auto |
| transformation__attribute_0_encoder__drop | |
| transformation__attribute_0_encoder__dtype | <class 'numpy.float64'> |
| transformation__attribute_0_encoder__handle_unknown | error |
| transformation__attribute_0_encoder__sparse | True |
| transformation__attribute_1_encoder__categories | auto |
| transformation__attribute_1_encoder__drop | |
| transformation__attribute_1_encoder__dtype | <class 'numpy.float64'> |
| transformation__attribute_1_encoder__handle_unknown | error |
| transformation__attribute_1_encoder__sparse | True |
| transformation__product_code_encoder__categories | auto |
| transformation__product_code_encoder__drop | |
| transformation__product_code_encoder__dtype | <class 'numpy.float64'> |
| transformation__product_code_encoder__handle_unknown | error |
| transformation__product_code_encoder__sparse | True |
| model__ccp_alpha | 0.0 |
| model__class_weight | |
| model__criterion | gini |
| model__max_depth | 4 |
| model__max_features | |
| model__max_leaf_nodes | |
| model__min_impurity_decrease | 0.0 |
| model__min_samples_leaf | 1 |
| model__min_samples_split | 2 |
| model__min_weight_fraction_leaf | 0.0 |
| model__random_state | |
| model__splitter | best |
</details>
### Model Plot
The model plot is below.
<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" width="100%"><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 width="100%"><div class="sk-item sk-dashed-wrapped" width="100%"><div class="sk-label-container" width="100%"><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" width="100%"><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" width="100%"><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" width="100%"><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" width="100%"><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" width="100%"><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" width="100%"><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>
## Evaluation Results
You can find the details about evaluation process and the evaluation results.
| Metric | Value |
|----------|---------|
| accuracy | 0.7888 |
| f1 score | 0.7888 |
# How to Get Started with the Model
Use the code below to get started with the model.
<details>
<summary> Click to expand </summary>
```python
import pickle
with open(decision-tree-playground-kaggle/model.pkl, 'rb') as file:
clf = pickle.load(file)
```
</details>
# Model Card Authors
This model card is written by following authors:
huggingface
# Model Card Contact
You can contact the model card authors through following channels:
[More Information Needed]
# Citation
Below you can find information related to citation.
**BibTeX:**
```
[More Information Needed]
```
Tree Plot
Confusion Matrix
|