--- 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.

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| Hyperparameter | Value | |-----------------------------------------------------|--------------------------------------------------------------------------------------------------------------------------------------------------------------------| | memory | | | steps | [('columntransformer', ColumnTransformer(remainder='passthrough',transformers=[('onehotencoder',OneHotEncoder(handle_unknown='ignore',sparse=False),)])), ('gradientboostingregressor', GradientBoostingRegressor(random_state=42))] | | verbose | False | | columntransformer | ColumnTransformer(remainder='passthrough',transformers=[('onehotencoder',OneHotEncoder(handle_unknown='ignore',sparse=False),)]) | | 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), )] | | 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 | | | 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 |

### Model Plot The model plot is below.

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))])

Please rerun this cell to show the HTML repr or trust the notebook.

# How to Get Started with the Model Use the code below to get started with the model.

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```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") ```

# Model Card Authors This model card is written by following authors: Brenden Connors