catboost_regressor¶
CatBoost Regressor, a regressor that uses gradient-boosting on decision trees. CatBoost is an open-source library and natively supports categorical features.
Module Contents¶
Classes Summary¶
CatBoost Regressor, a regressor that uses gradient-boosting on decision trees. CatBoost is an open-source library and natively supports categorical features. |
Contents¶
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class
evalml.pipelines.components.estimators.regressors.catboost_regressor.
CatBoostRegressor
(n_estimators=10, eta=0.03, max_depth=6, bootstrap_type=None, silent=False, allow_writing_files=False, random_seed=0, n_jobs=- 1, **kwargs)[source]¶ CatBoost Regressor, a regressor that uses gradient-boosting on decision trees. CatBoost is an open-source library and natively supports categorical features.
For more information, check out https://catboost.ai/
- Parameters
n_estimators (float) – The maximum number of trees to build. Defaults to 10.
eta (float) – The learning rate. Defaults to 0.03.
max_depth (int) – The maximum tree depth for base learners. Defaults to 6.
bootstrap_type (string) – Defines the method for sampling the weights of objects. Available methods are ‘Bayesian’, ‘Bernoulli’, ‘MVS’. Defaults to None.
silent (boolean) – Whether to use the “silent” logging mode. Defaults to True.
allow_writing_files (boolean) – Whether to allow writing snapshot files while training. Defaults to False.
n_jobs (int or None) – Number of jobs to run in parallel. -1 uses all processes. Defaults to -1.
random_seed (int) – Seed for the random number generator. Defaults to 0.
Attributes
hyperparameter_ranges
{ “n_estimators”: Integer(4, 100), “eta”: Real(0.000001, 1), “max_depth”: Integer(4, 10),}
model_family
ModelFamily.CATBOOST
modifies_features
True
modifies_target
False
name
CatBoost Regressor
predict_uses_y
False
supported_problem_types
[ ProblemTypes.REGRESSION, ProblemTypes.TIME_SERIES_REGRESSION,]
training_only
False
Methods
Constructs a new component with the same parameters and random state.
Returns the default parameters for this component.
Describe a component and its parameters.
Feature importance of fitted CatBoost regressor.
Fits CatBoost regressor component to data.
Loads component at file path.
Returns boolean determining if component needs fitting before calling predict, predict_proba, transform, or feature_importances.
Returns the parameters which were used to initialize the component.
Make predictions using selected features.
Make probability estimates for labels.
Saves component at file path.
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clone
(self)¶ Constructs a new component with the same parameters and random state.
- Returns
A new instance of this component with identical parameters and random state.
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default_parameters
(cls)¶ Returns the default parameters for this component.
Our convention is that Component.default_parameters == Component().parameters.
- Returns
Default parameters for this component.
- Return type
dict
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describe
(self, print_name=False, return_dict=False)¶ Describe a component and its parameters.
- Parameters
print_name (bool, optional) – whether to print name of component
return_dict (bool, optional) – whether to return description as dictionary in the format {“name”: name, “parameters”: parameters}
- Returns
Returns dictionary if return_dict is True, else None.
- Return type
None or dict
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property
feature_importance
(self)¶ Feature importance of fitted CatBoost regressor.
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fit
(self, X, y=None)[source]¶ Fits CatBoost regressor component to data.
- Parameters
X (pd.DataFrame) – The input training data of shape [n_samples, n_features].
y (pd.Series) – The target training data of length [n_samples].
- Returns
self
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static
load
(file_path)¶ Loads component at file path.
- Parameters
file_path (str) – Location to load file.
- Returns
ComponentBase object
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needs_fitting
(self)¶ Returns boolean determining if component needs fitting before calling predict, predict_proba, transform, or feature_importances.
This can be overridden to False for components that do not need to be fit or whose fit methods do nothing.
- Returns
True.
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property
parameters
(self)¶ Returns the parameters which were used to initialize the component.
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predict
(self, X)¶ Make predictions using selected features.
- Parameters
X (pd.DataFrame) – Data of shape [n_samples, n_features].
- Returns
Predicted values.
- Return type
pd.Series
- Raises
MethodPropertyNotFoundError – If estimator does not have a predict method or a component_obj that implements predict.
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predict_proba
(self, X)¶ Make probability estimates for labels.
- Parameters
X (pd.DataFrame) – Features.
- Returns
Probability estimates.
- Return type
pd.Series
- Raises
MethodPropertyNotFoundError – If estimator does not have a predict_proba method or a component_obj that implements predict_proba.
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save
(self, file_path, pickle_protocol=cloudpickle.DEFAULT_PROTOCOL)¶ Saves component at file path.
- Parameters
file_path (str) – Location to save file.
pickle_protocol (int) – The pickle data stream format.