| --- |
| library_name: sklearn |
| license: mit |
| tags: |
| - sklearn |
| - skops |
| - tabular-regression |
| model_format: pickle |
| model_file: example.pkl |
| widget: |
| - structuredData: |
| Height: |
| - 11.52 |
| - 12.48 |
| - 12.3778 |
| Length1: |
| - 23.2 |
| - 24.0 |
| - 23.9 |
| Length2: |
| - 25.4 |
| - 26.3 |
| - 26.5 |
| Length3: |
| - 30.0 |
| - 31.2 |
| - 31.1 |
| Species: |
| - Bream |
| - Bream |
| - Bream |
| Width: |
| - 4.02 |
| - 4.3056 |
| - 4.6961 |
| --- |
| |
| # Model description |
|
|
| [More Information Needed] |
|
|
| ## Intended uses & limitations |
|
|
| [More Information Needed] |
|
|
| ## Training Procedure |
|
|
| [More Information Needed] |
|
|
| ### Hyperparameters |
|
|
| <details> |
| <summary> Click to expand </summary> |
|
|
| | Hyperparameter | Value | |
| |---------------------------------------------------------|----------------------------------------------------------------------------------------------------------------------------------------------------------------| |
| | memory | | |
| | steps | [('columntransformer', ColumnTransformer(remainder='passthrough',<br /> transformers=[('onehotencoder',<br /> OneHotEncoder(handle_unknown='ignore',<br /> sparse=False),<br /> <sklearn.compose._column_transformer.make_column_selector object at 0x7c049c39ec20>)])), ('gradientboostingregressor', GradientBoostingRegressor(random_state=42))] | |
| | verbose | False | |
| | columntransformer | ColumnTransformer(remainder='passthrough',<br /> transformers=[('onehotencoder',<br /> OneHotEncoder(handle_unknown='ignore',<br /> sparse=False),<br /> <sklearn.compose._column_transformer.make_column_selector object at 0x7c049c39ec20>)]) | |
| | gradientboostingregressor | GradientBoostingRegressor(random_state=42) | |
| | columntransformer__n_jobs | | |
| | columntransformer__remainder | passthrough | |
| | columntransformer__sparse_threshold | 0.3 | |
| | columntransformer__transformer_weights | | |
| | columntransformer__transformers | [('onehotencoder', OneHotEncoder(handle_unknown='ignore', sparse=False), <sklearn.compose._column_transformer.make_column_selector object at 0x7c049c39ec20>)] | |
| | columntransformer__verbose | False | |
| | columntransformer__verbose_feature_names_out | True | |
| | columntransformer__onehotencoder | OneHotEncoder(handle_unknown='ignore', sparse=False) | |
| | columntransformer__onehotencoder__categories | auto | |
| | columntransformer__onehotencoder__drop | | |
| | columntransformer__onehotencoder__dtype | <class 'numpy.float64'> | |
| | columntransformer__onehotencoder__feature_name_combiner | concat | |
| | columntransformer__onehotencoder__handle_unknown | ignore | |
| | columntransformer__onehotencoder__max_categories | | |
| | columntransformer__onehotencoder__min_frequency | | |
| | columntransformer__onehotencoder__sparse | False | |
| | columntransformer__onehotencoder__sparse_output | True | |
| | gradientboostingregressor__alpha | 0.9 | |
| | gradientboostingregressor__ccp_alpha | 0.0 | |
| | gradientboostingregressor__criterion | friedman_mse | |
| | gradientboostingregressor__init | | |
| | gradientboostingregressor__learning_rate | 0.1 | |
| | gradientboostingregressor__loss | squared_error | |
| | gradientboostingregressor__max_depth | 3 | |
| | gradientboostingregressor__max_features | | |
| | gradientboostingregressor__max_leaf_nodes | | |
| | gradientboostingregressor__min_impurity_decrease | 0.0 | |
| | gradientboostingregressor__min_samples_leaf | 1 | |
| | gradientboostingregressor__min_samples_split | 2 | |
| | gradientboostingregressor__min_weight_fraction_leaf | 0.0 | |
| | gradientboostingregressor__n_estimators | 100 | |
| | gradientboostingregressor__n_iter_no_change | | |
| | gradientboostingregressor__random_state | 42 | |
| | gradientboostingregressor__subsample | 1.0 | |
| | gradientboostingregressor__tol | 0.0001 | |
| | gradientboostingregressor__validation_fraction | 0.1 | |
| | gradientboostingregressor__verbose | 0 | |
| | gradientboostingregressor__warm_start | False | |
| |
| </details> |
| |
| ### Model Plot |
| |
| <style>#sk-container-id-3 {color: black;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-3 label.sk-toggleable__label-arrow:before {content: "▸";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: "▾";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 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-container-id-3 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-container-id-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: "";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-serial::before {content: "";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-3 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 div.sk-parallel-item::before {content: "";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 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;}#sk-container-id-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 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-container-id-3 div.sk-text-repr-fallback {display: none;}</style><div id="sk-container-id-3" class="sk-top-container" style="overflow: auto;"><div class="sk-text-repr-fallback"><pre>Pipeline(steps=[('columntransformer',ColumnTransformer(remainder='passthrough',transformers=[('onehotencoder',OneHotEncoder(handle_unknown='ignore',sparse=False),<sklearn.compose._column_transformer.make_column_selector object at 0x7c049c39ec20>)])),('gradientboostingregressor',GradientBoostingRegressor(random_state=42))])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</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="sk-estimator-id-15" type="checkbox" ><label for="sk-estimator-id-15" class="sk-toggleable__label sk-toggleable__label-arrow">Pipeline</label><div class="sk-toggleable__content"><pre>Pipeline(steps=[('columntransformer',ColumnTransformer(remainder='passthrough',transformers=[('onehotencoder',OneHotEncoder(handle_unknown='ignore',sparse=False),<sklearn.compose._column_transformer.make_column_selector object at 0x7c049c39ec20>)])),('gradientboostingregressor',GradientBoostingRegressor(random_state=42))])</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="sk-estimator-id-16" type="checkbox" ><label for="sk-estimator-id-16" class="sk-toggleable__label sk-toggleable__label-arrow">columntransformer: ColumnTransformer</label><div class="sk-toggleable__content"><pre>ColumnTransformer(remainder='passthrough',transformers=[('onehotencoder',OneHotEncoder(handle_unknown='ignore',sparse=False),<sklearn.compose._column_transformer.make_column_selector object at 0x7c049c39ec20>)])</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="sk-estimator-id-17" type="checkbox" ><label for="sk-estimator-id-17" class="sk-toggleable__label sk-toggleable__label-arrow">onehotencoder</label><div class="sk-toggleable__content"><pre><sklearn.compose._column_transformer.make_column_selector object at 0x7c049c39ec20></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="sk-estimator-id-18" type="checkbox" ><label for="sk-estimator-id-18" class="sk-toggleable__label sk-toggleable__label-arrow">OneHotEncoder</label><div class="sk-toggleable__content"><pre>OneHotEncoder(handle_unknown='ignore', sparse=False)</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="sk-estimator-id-19" type="checkbox" ><label for="sk-estimator-id-19" class="sk-toggleable__label sk-toggleable__label-arrow">remainder</label><div class="sk-toggleable__content"><pre>['Length1', 'Length2', 'Length3', 'Height', 'Width']</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="sk-estimator-id-20" type="checkbox" ><label for="sk-estimator-id-20" class="sk-toggleable__label sk-toggleable__label-arrow">passthrough</label><div class="sk-toggleable__content"><pre>passthrough</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="sk-estimator-id-21" type="checkbox" ><label for="sk-estimator-id-21" class="sk-toggleable__label sk-toggleable__label-arrow">GradientBoostingRegressor</label><div class="sk-toggleable__content"><pre>GradientBoostingRegressor(random_state=42)</pre></div></div></div></div></div></div></div> |
| |
| ## Evaluation Results |
| |
| [More Information Needed] |
| |
| # How to Get Started with the Model |
| |
| [More Information Needed] |
| |
| # Model Card Authors |
| |
| This model card is written by following authors: |
| |
| [More Information Needed] |
| |
| # 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] |
| ``` |
| |
| # model_card_authors |
| |
| JP |
| |
| # limitations |
| |
| This model is intended for educational purposes. |
| |
| # model_description |
| |
| This is a GradientBoostingRegressor on a fish dataset. |
| |
