scikit-learn/tabular-playground
3
1---2library_name: sklearn3tags:4- sklearn5- skops6- tabular-classification7widget:8 structuredData:9 attribute_0:10 - material_711 - material_712 - material_713 attribute_1:14 - material_815 - material_816 - material_617 attribute_2:18 - 519 - 520 - 621 attribute_3:22 - 823 - 824 - 925 loading:26 - 154.0227 - 108.7328 - 99.8429 measurement_0:30 - 1431 - 432 - 633 measurement_1:34 - 635 - 736 - 737 measurement_10:38 - 16.63739 - 16.20740 - 17.1741 measurement_11:42 - 20.71943 - 20.05844 - 20.85845 measurement_12:46 - 12.82447 - 11.89848 - 10.96849 measurement_13:50 - 16.06751 - 13.87152 - 16.44853 measurement_14:54 - 15.18155 - 14.26656 - 15.657 measurement_15:58 - 18.54659 - 15.73460 - 14.63761 measurement_16:62 - 19.40263 - 16.88664 - 13.8665 measurement_17:66 - 643.08667 - 642.53368 - 673.54569 measurement_2:70 - 671 - 972 - 673 measurement_3:74 - 19.53275 - 18.12876 - NaN77 measurement_4:78 - 11.01779 - 11.86680 - 10.06481 measurement_5:82 - 15.63983 - 17.89184 - 16.28785 measurement_6:86 - 16.70987 - 20.30288 - 17.44589 measurement_7:90 - 10.05791 - NaN92 - 12.11793 measurement_8:94 - 20.20195 - 18.14896 - 20.65997 measurement_9:98 - 11.10699 - 10.221100 - 11.999101 product_code:102 - C103 - C104 - E105---106 107# Model description108 109This is a DecisionTreeClassifier model built for Kaggle Tabular Playground Series August 2022, trained on supersoaker production failures dataset.110 111## Intended uses & limitations112 113This model is not ready to be used in production.114 115## Training Procedure116 117### Hyperparameters118 119The 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', 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))] |128| verbose | False |129| 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(),130'attribute_1']),('product_code_encoder', OneHotEncoder(),['product_code'])]) |131| model | DecisionTreeClassifier(max_depth=4) |132| transformation__n_jobs | |133| transformation__remainder | drop |134| transformation__sparse_threshold | 0.3 |135| transformation__transformer_weights | |136| 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'])]137|138| transformation__verbose | False |139| transformation__verbose_feature_names_out | True |140| transformation__loading_missing_value_imputer | SimpleImputer() |141| transformation__numerical_missing_value_imputer | SimpleImputer() |142| transformation__attribute_0_encoder | OneHotEncoder() |143| transformation__attribute_1_encoder | OneHotEncoder() |144| transformation__product_code_encoder | OneHotEncoder() |145| transformation__loading_missing_value_imputer__add_indicator | False |146| transformation__loading_missing_value_imputer__copy | True |147| transformation__loading_missing_value_imputer__fill_value | |148| transformation__loading_missing_value_imputer__missing_values | nan |149| transformation__loading_missing_value_imputer__strategy | mean |150| transformation__loading_missing_value_imputer__verbose | 0 |151| transformation__numerical_missing_value_imputer__add_indicator | False |152| transformation__numerical_missing_value_imputer__copy | True |153| transformation__numerical_missing_value_imputer__fill_value | |154| transformation__numerical_missing_value_imputer__missing_values | nan |155| transformation__numerical_missing_value_imputer__strategy | mean |156| transformation__numerical_missing_value_imputer__verbose | 0 |157| transformation__attribute_0_encoder__categories | auto |158| transformation__attribute_0_encoder__drop | |159| transformation__attribute_0_encoder__dtype | <class 'numpy.float64'> |160| transformation__attribute_0_encoder__handle_unknown | error |161| transformation__attribute_0_encoder__sparse | True |162| transformation__attribute_1_encoder__categories | auto |163| transformation__attribute_1_encoder__drop | |164| transformation__attribute_1_encoder__dtype | <class 'numpy.float64'> |165| transformation__attribute_1_encoder__handle_unknown | error |166| transformation__attribute_1_encoder__sparse | True |167| transformation__product_code_encoder__categories | auto |168| transformation__product_code_encoder__drop | |169| transformation__product_code_encoder__dtype | <class 'numpy.float64'> |170| transformation__product_code_encoder__handle_unknown | error |171| transformation__product_code_encoder__sparse | True |172| model__ccp_alpha | 0.0 |173| model__class_weight | |174| model__criterion | gini |175| model__max_depth | 4 |176| model__max_features | |177| model__max_leaf_nodes | |178| model__min_impurity_decrease | 0.0 |179| model__min_samples_leaf | 1 |180| model__min_samples_split | 2 |181| model__min_weight_fraction_leaf | 0.0 |182| model__random_state | |183| model__splitter | best |184 185</details>186 187### Model Plot188 189The model plot is below.190 191<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>192 193 194Evaluation Results195 196You can find the details about evaluation process and the evaluation results.197 198 199 200| Metric | Value |201|----------|---------|202| accuracy | 0.7888 |203| f1 score | 0.7888 |204 205# How to Get Started with the Model206 207Use the code below to get started with the model.208 209<details>210<summary> Click to expand </summary>211 212```python213import pickle 214with open(decision-tree-playground-kaggle/model.pkl, 'rb') as file: 215 clf = pickle.load(file)216```217 218</details>219 220 221 222 223# Model Card Authors224 225This model card is written by following authors:226 227huggingface228 229# Model Card Contact230 231You can contact the model card authors through following channels:232[More Information Needed]233 234# Citation235 236Below you can find information related to citation.237 238**BibTeX:**239```240[More Information Needed]241```242 243 244Tree Plot245246 247 248 249Confusion Matrix250251