
# Model description

This is a copy of (tabular-playground)[https://huggingface.co/scikit-learn/tabular-playground] for testing purposes.

## 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"><div class="sk-text-repr-fallback"><pre>Pipeline(steps=[(&#x27;transformation&#x27;,ColumnTransformer(transformers=[(&#x27;loading_missing_value_imputer&#x27;,SimpleImputer(),[&#x27;loading&#x27;]),(&#x27;numerical_missing_value_imputer&#x27;,SimpleImputer(),[&#x27;loading&#x27;, &#x27;measurement_3&#x27;,&#x27;measurement_4&#x27;,&#x27;measurement_5&#x27;,&#x27;measurement_6&#x27;,&#x27;measurement_7&#x27;,&#x27;measurement_8&#x27;,&#x27;measurement_9&#x27;,&#x27;measurement_10&#x27;,&#x27;measurement_11&#x27;,&#x27;measurement_12&#x27;,&#x27;measurement_13&#x27;,&#x27;measurement_14&#x27;,&#x27;measurement_15&#x27;,&#x27;measurement_16&#x27;,&#x27;measurement_17&#x27;]),(&#x27;attribute_0_encoder&#x27;,OneHotEncoder(),[&#x27;attribute_0&#x27;]),(&#x27;attribute_1_encoder&#x27;,OneHotEncoder(),[&#x27;attribute_1&#x27;]),(&#x27;product_code_encoder&#x27;,OneHotEncoder(),[&#x27;product_code&#x27;])])),(&#x27;model&#x27;, 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=[(&#x27;transformation&#x27;,ColumnTransformer(transformers=[(&#x27;loading_missing_value_imputer&#x27;,SimpleImputer(),[&#x27;loading&#x27;]),(&#x27;numerical_missing_value_imputer&#x27;,SimpleImputer(),[&#x27;loading&#x27;, &#x27;measurement_3&#x27;,&#x27;measurement_4&#x27;,&#x27;measurement_5&#x27;,&#x27;measurement_6&#x27;,&#x27;measurement_7&#x27;,&#x27;measurement_8&#x27;,&#x27;measurement_9&#x27;,&#x27;measurement_10&#x27;,&#x27;measurement_11&#x27;,&#x27;measurement_12&#x27;,&#x27;measurement_13&#x27;,&#x27;measurement_14&#x27;,&#x27;measurement_15&#x27;,&#x27;measurement_16&#x27;,&#x27;measurement_17&#x27;]),(&#x27;attribute_0_encoder&#x27;,OneHotEncoder(),[&#x27;attribute_0&#x27;]),(&#x27;attribute_1_encoder&#x27;,OneHotEncoder(),[&#x27;attribute_1&#x27;]),(&#x27;product_code_encoder&#x27;,OneHotEncoder(),[&#x27;product_code&#x27;])])),(&#x27;model&#x27;, 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=[(&#x27;loading_missing_value_imputer&#x27;,SimpleImputer(), [&#x27;loading&#x27;]),(&#x27;numerical_missing_value_imputer&#x27;,SimpleImputer(),[&#x27;loading&#x27;, &#x27;measurement_3&#x27;, &#x27;measurement_4&#x27;,&#x27;measurement_5&#x27;, &#x27;measurement_6&#x27;,&#x27;measurement_7&#x27;, &#x27;measurement_8&#x27;,&#x27;measurement_9&#x27;, &#x27;measurement_10&#x27;,&#x27;measurement_11&#x27;, &#x27;measurement_12&#x27;,&#x27;measurement_13&#x27;, &#x27;measurement_14&#x27;,&#x27;measurement_15&#x27;, &#x27;measurement_16&#x27;,&#x27;measurement_17&#x27;]),(&#x27;attribute_0_encoder&#x27;, OneHotEncoder(),[&#x27;attribute_0&#x27;]),(&#x27;attribute_1_encoder&#x27;, OneHotEncoder(),[&#x27;attribute_1&#x27;]),(&#x27;product_code_encoder&#x27;, OneHotEncoder(),[&#x27;product_code&#x27;])])</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>[&#x27;loading&#x27;]</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>[&#x27;loading&#x27;, &#x27;measurement_3&#x27;, &#x27;measurement_4&#x27;, &#x27;measurement_5&#x27;, &#x27;measurement_6&#x27;, &#x27;measurement_7&#x27;, &#x27;measurement_8&#x27;, &#x27;measurement_9&#x27;, &#x27;measurement_10&#x27;, &#x27;measurement_11&#x27;, &#x27;measurement_12&#x27;, &#x27;measurement_13&#x27;, &#x27;measurement_14&#x27;, &#x27;measurement_15&#x27;, &#x27;measurement_16&#x27;, &#x27;measurement_17&#x27;]</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>[&#x27;attribute_0&#x27;]</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>[&#x27;attribute_1&#x27;]</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>[&#x27;product_code&#x27;]</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
![Tree Plot](tree.png)



Confusion Matrix
![Confusion Matrix](confusion_matrix.png)

{"library_name": "sklearn", "tags": ["sklearn", "skops", "tabular-classification"], "widget": {"structuredData": {}}}
sklearn skops tabular-classification region:us
TAGS
#sklearn #skops #tabular-classification #region-us 

Model description
=================


This is a copy of (tabular-playground)[URL for testing purposes.


Intended uses & limitations
---------------------------


This model is not ready to be used in production.


Training Procedure
------------------


### Hyperparameters


The model is trained with below hyperparameters.



 Click to expand 


```
                             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 |



### Model Plot


The model plot is below.


#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 URL-toggleable {background-color: white;}#sk-b8914d13-cacb-404b-89fd-48f0ed8d671f URL-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 URL-toggleable\_\_label-arrow:before {content: "▸";float: left;margin-right: 0.25em;color: #696969;}#sk-b8914d13-cacb-404b-89fd-48f0ed8d671f URL-toggleable\_\_label-arrow:hover:before {color: black;}#sk-b8914d13-cacb-404b-89fd-48f0ed8d671f URL-estimator:hover URL-toggleable\_\_label-arrow:before {color: black;}#sk-b8914d13-cacb-404b-89fd-48f0ed8d671f URL-toggleable\_\_content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-b8914d13-cacb-404b-89fd-48f0ed8d671f URL-toggleable\_\_content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-b8914d13-cacb-404b-89fd-48f0ed8d671f URL-toggleable\_\_control:checked~URL-toggleable\_\_content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-b8914d13-cacb-404b-89fd-48f0ed8d671f URL-toggleable\_\_control:checked~URL-toggleable\_\_label-arrow:before {content: "▾";}#sk-b8914d13-cacb-404b-89fd-48f0ed8d671f URL-estimator URL-toggleable\_\_control:checked~URL-toggleable\_\_label {background-color: #d4ebff;}#sk-b8914d13-cacb-404b-89fd-48f0ed8d671f URL-label URL-toggleable\_\_control:checked~URL-toggleable\_\_label {background-color: #d4ebff;}#sk-b8914d13-cacb-404b-89fd-48f0ed8d671f URL-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 URL-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 URL-estimator:hover {background-color: #d4ebff;}#sk-b8914d13-cacb-404b-89fd-48f0ed8d671f URL-parallel-item::after {content: "";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-b8914d13-cacb-404b-89fd-48f0ed8d671f URL-label:hover URL-toggleable\_\_label {background-color: #d4ebff;}#sk-b8914d13-cacb-404b-89fd-48f0ed8d671f URL-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 URL-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 URL-item {z-index: 1;}#sk-b8914d13-cacb-404b-89fd-48f0ed8d671f URL-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;}#sk-b8914d13-cacb-404b-89fd-48f0ed8d671f URL-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 URL-parallel-item {display: flex;flex-direction: column;position: relative;background-color: white;}#sk-b8914d13-cacb-404b-89fd-48f0ed8d671f URL-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-b8914d13-cacb-404b-89fd-48f0ed8d671f URL-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-b8914d13-cacb-404b-89fd-48f0ed8d671f URL-parallel-item:only-child::after {width: 0;}#sk-b8914d13-cacb-404b-89fd-48f0ed8d671f URL-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 URL-label label {font-family: monospace;font-weight: bold;background-color: white;display: inline-block;line-height: 1.2em;}#sk-b8914d13-cacb-404b-89fd-48f0ed8d671f URL-label-container {position: relative;z-index: 2;text-align: center;}#sk-b8914d13-cacb-404b-89fd-48f0ed8d671f URL-container {/\* jupyter's 'URL' sets '[hidden] { display: none; }' but URL 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 URL. See: URL \*/display: inline-block !important;position: relative;}#sk-b8914d13-cacb-404b-89fd-48f0ed8d671f URL-text-repr-fallback {display: none;}
```
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))])
```
**Please rerun this cell to show the HTML repr or trust the notebook.**Pipeline
```
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))])
```
transformation: ColumnTransformer
```
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'])])
```
loading\_missing\_value\_imputer
```
['loading']
```
SimpleImputer
```
SimpleImputer()
```
numerical\_missing\_value\_imputer
```
['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']
```
SimpleImputer
```
SimpleImputer()
```
attribute\_0\_encoder
```
['attribute_0']
```
OneHotEncoder
```
OneHotEncoder()
```
attribute\_1\_encoder
```
['attribute_1']
```
OneHotEncoder
```
OneHotEncoder()
```
product\_code\_encoder
```
['product_code']
```
OneHotEncoder
```
OneHotEncoder()
```
DecisionTreeClassifier
```
DecisionTreeClassifier(max_depth=4)
```

## Evaluation Results


You can find the details about evaluation process and the evaluation results.



How to Get Started with the Model
=================================


Use the code below to get started with the model.



 Click to expand 

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:


Below you can find information related to citation.


BibTeX:


Tree Plot
!Tree Plot


Confusion Matrix
!Confusion Matrix



### Hyperparameters


The model is trained with below hyperparameters.



 Click to expand 


```
                             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 | ### Model Plot


The model plot is below.
TAGS
#sklearn #skops #tabular-classification #region-us 
 ### Hyperparameters


The model is trained with below hyperparameters.



 Click to expand 


```
                             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 | ### Model Plot


The model plot is below.
passage: TAGS
#sklearn #skops #tabular-classification #region-us 
