| --- |
| license: mit |
| library_name: sklearn |
| tags: |
| - sklearn |
| - skops |
| - tabular-regression |
| 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 |
|
|
| This is a GradientBoostingRegressor on a fish dataset. |
|
|
| ## Intended uses & limitations |
|
|
| This model is intended for educational purposes. |
|
|
|
|
| ### Hyperparameters |
|
|
| The model is trained with below hyperparameters. |
|
|
| <details> |
| <summary> Click to expand </summary> |
|
|
| | Hyperparameter | Value | |
| |-----------------------------------------------------|--------------------------------------------------------------------------------------------------------------------------------------------------------------------| |
| | memory | | |
| | steps | [('columntransformer', ColumnTransformer(remainder='passthrough',transformers=[('onehotencoder',OneHotEncoder(handle_unknown='ignore',sparse=False),<sklearn.compose._column_transformer.make_column_selector object at 0x000001E750BBC6A0>)])), ('gradientboostingregressor', GradientBoostingRegressor(random_state=42))] | |
| | verbose | False | |
| | columntransformer | ColumnTransformer(remainder='passthrough',transformers=[('onehotencoder',OneHotEncoder(handle_unknown='ignore',sparse=False),<sklearn.compose._column_transformer.make_column_selector object at 0x000001E750BBC6A0>)]) | |
| | 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 0x000001E750BBC6A0>)] | |
| | 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__handle_unknown | ignore | |
| | columntransformer__onehotencoder__sparse | False | |
| | 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 |
| |
| The model plot is below. |
| |
| <style>#sk-ccf5150a-bed5-4d7b-a5a9-a1a6d13a1794 {color: black;background-color: white;}#sk-ccf5150a-bed5-4d7b-a5a9-a1a6d13a1794 pre{padding: 0;}#sk-ccf5150a-bed5-4d7b-a5a9-a1a6d13a1794 div.sk-toggleable {background-color: white;}#sk-ccf5150a-bed5-4d7b-a5a9-a1a6d13a1794 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-ccf5150a-bed5-4d7b-a5a9-a1a6d13a1794 label.sk-toggleable__label-arrow:before {content: "▸";float: left;margin-right: 0.25em;color: #696969;}#sk-ccf5150a-bed5-4d7b-a5a9-a1a6d13a1794 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-ccf5150a-bed5-4d7b-a5a9-a1a6d13a1794 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-ccf5150a-bed5-4d7b-a5a9-a1a6d13a1794 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-ccf5150a-bed5-4d7b-a5a9-a1a6d13a1794 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-ccf5150a-bed5-4d7b-a5a9-a1a6d13a1794 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-ccf5150a-bed5-4d7b-a5a9-a1a6d13a1794 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: "▾";}#sk-ccf5150a-bed5-4d7b-a5a9-a1a6d13a1794 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-ccf5150a-bed5-4d7b-a5a9-a1a6d13a1794 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-ccf5150a-bed5-4d7b-a5a9-a1a6d13a1794 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-ccf5150a-bed5-4d7b-a5a9-a1a6d13a1794 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-ccf5150a-bed5-4d7b-a5a9-a1a6d13a1794 div.sk-estimator:hover {background-color: #d4ebff;}#sk-ccf5150a-bed5-4d7b-a5a9-a1a6d13a1794 div.sk-parallel-item::after {content: "";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-ccf5150a-bed5-4d7b-a5a9-a1a6d13a1794 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-ccf5150a-bed5-4d7b-a5a9-a1a6d13a1794 div.sk-serial::before {content: "";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 2em;bottom: 0;left: 50%;}#sk-ccf5150a-bed5-4d7b-a5a9-a1a6d13a1794 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;}#sk-ccf5150a-bed5-4d7b-a5a9-a1a6d13a1794 div.sk-item {z-index: 1;}#sk-ccf5150a-bed5-4d7b-a5a9-a1a6d13a1794 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;}#sk-ccf5150a-bed5-4d7b-a5a9-a1a6d13a1794 div.sk-parallel::before {content: "";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 2em;bottom: 0;left: 50%;}#sk-ccf5150a-bed5-4d7b-a5a9-a1a6d13a1794 div.sk-parallel-item {display: flex;flex-direction: column;position: relative;background-color: white;}#sk-ccf5150a-bed5-4d7b-a5a9-a1a6d13a1794 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-ccf5150a-bed5-4d7b-a5a9-a1a6d13a1794 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-ccf5150a-bed5-4d7b-a5a9-a1a6d13a1794 div.sk-parallel-item:only-child::after {width: 0;}#sk-ccf5150a-bed5-4d7b-a5a9-a1a6d13a1794 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-ccf5150a-bed5-4d7b-a5a9-a1a6d13a1794 div.sk-label label {font-family: monospace;font-weight: bold;background-color: white;display: inline-block;line-height: 1.2em;}#sk-ccf5150a-bed5-4d7b-a5a9-a1a6d13a1794 div.sk-label-container {position: relative;z-index: 2;text-align: center;}#sk-ccf5150a-bed5-4d7b-a5a9-a1a6d13a1794 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-ccf5150a-bed5-4d7b-a5a9-a1a6d13a1794 div.sk-text-repr-fallback {display: none;}</style><div id="sk-ccf5150a-bed5-4d7b-a5a9-a1a6d13a1794" class="sk-top-container"><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 0x000001E750BBC6A0>)])),('gradientboostingregressor',GradientBoostingRegressor(random_state=42))])</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="f6612892-c085-4dd9-8dca-9cb8081c3777" type="checkbox" ><label for="f6612892-c085-4dd9-8dca-9cb8081c3777" 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 0x000001E750BBC6A0>)])),('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="3d74f98b-ae31-452d-af87-2c65b0323ba2" type="checkbox" ><label for="3d74f98b-ae31-452d-af87-2c65b0323ba2" 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 0x000001E750BBC6A0>)])</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="4af39992-03cf-4522-a288-2db0a787a63c" type="checkbox" ><label for="4af39992-03cf-4522-a288-2db0a787a63c" 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 0x000001E750BBC6A0></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="519d5e51-5fa6-45d6-a3f7-59c11370402d" type="checkbox" ><label for="519d5e51-5fa6-45d6-a3f7-59c11370402d" 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="7ede29a7-2614-4eed-a021-e85f1aaa5659" type="checkbox" ><label for="7ede29a7-2614-4eed-a021-e85f1aaa5659" 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="69357535-0314-4987-a311-112335d2cb52" type="checkbox" ><label for="69357535-0314-4987-a311-112335d2cb52" 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="f247fbf2-2247-4e99-aaa2-f6fb89ce1b13" type="checkbox" ><label for="f247fbf2-2247-4e99-aaa2-f6fb89ce1b13" 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> |
| |
| |
| # How to Get Started with the Model |
| |
| Use the code below to get started with the model. |
| |
| <details> |
| <summary> Click to expand </summary> |
| |
| ```python |
| from skops.hub_utils import download |
| from skops.io import load |
| |
| download("brendenc/Fish-Weight", "path_to_folder") |
| # make sure model file is in skops format |
| # if model is a pickle file, make sure it's from a source you trust |
| model = load("path_to_folder/example.pkl") |
| ``` |
| |
| </details> |
| |
| |
| |
| # Model Card Authors |
| |
| This model card is written by following authors: |
| |
| Brenden Connors |
| |
| |
