scikit-learn/Fish-Weight

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.

Click to expand

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

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.

Click to expand

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