metadata
license: mit
library_name: sklearn
tags:
- sklearn
- skops
- tabular-regression
widget:
structuredData:
Height:
- 11.52
- 12.48
- 12.3778
Length1:
- 23.2
- 24
- 23.9
Length2:
- 25.4
- 26.3
- 26.5
Length3:
- 30
- 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.
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.
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))])
ColumnTransformer(remainder='passthrough',transformers=[('onehotencoder',OneHotEncoder(handle_unknown='ignore',sparse=False),<sklearn.compose._column_transformer.make_column_selector object at 0x000001E750BBC6A0>)])
<sklearn.compose._column_transformer.make_column_selector object at 0x000001E750BBC6A0>
OneHotEncoder(handle_unknown='ignore', sparse=False)
['Length1', 'Length2', 'Length3', 'Height', 'Width']
passthrough
GradientBoostingRegressor(random_state=42)
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