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.
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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>
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
