catboost_classifier

CatBoost Classifier, a classifier that uses gradient-boosting on decision trees. CatBoost is an open-source library and natively supports categorical features.

Module Contents

Classes Summary

CatBoostClassifier

CatBoost Classifier, a classifier that uses gradient-boosting on decision trees. CatBoost is an open-source library and natively supports categorical features.

Contents

class evalml.pipelines.components.estimators.classifiers.catboost_classifier.CatBoostClassifier(n_estimators=10, eta=0.03, max_depth=6, bootstrap_type=None, silent=True, allow_writing_files=False, random_seed=0, n_jobs=- 1, **kwargs)

CatBoost Classifier, a classifier 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 Classifier
supported_problem_types	[ ProblemTypes.BINARY, ProblemTypes.MULTICLASS, ProblemTypes.TIME_SERIES_BINARY, ProblemTypes.TIME_SERIES_MULTICLASS,]
training_only	False

Methods

clone	Constructs a new component with the same parameters and random state.
default_parameters	Returns the default parameters for this component.
describe	Describe a component and its parameters.
feature_importance	Feature importance of fitted CatBoost classifier.
fit	Fits CatBoost classifier component to data.
get_prediction_intervals	Find the prediction intervals using the fitted regressor.
load	Loads component at file path.
needs_fitting	Returns boolean determining if component needs fitting before calling predict, predict_proba, transform, or feature_importances.
parameters	Returns the parameters which were used to initialize the component.
predict	Make predictions using the fitted CatBoost classifier.
predict_proba	Make prediction probabilities using the fitted CatBoost classifier.
save	Saves component at file path.
update_parameters	Updates the parameter dictionary of the component.

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.

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

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

property feature_importance(self)

Feature importance of fitted CatBoost classifier.

fit(self, X, y=None)

Fits CatBoost classifier 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

get_prediction_intervals(self, X: pandas.DataFrame, y: Optional[pandas.Series] = None, coverage: List[float] = None, predictions: pandas.Series = None) → Dict[str, pandas.Series]

Find the prediction intervals using the fitted regressor.

This function takes the predictions of the fitted estimator and calculates the rolling standard deviation across all predictions using a window size of 5. The lower and upper predictions are determined by taking the percent point (quantile) function of the lower tail probability at each bound multiplied by the rolling standard deviation.

Parameters

• X (pd.DataFrame) – Data of shape [n_samples, n_features].

• y (pd.Series) – Target data. Ignored.

• coverage (list[float]) – A list of floats between the values 0 and 1 that the upper and lower bounds of the prediction interval should be calculated for.

• predictions (pd.Series) – Optional list of predictions to use. If None, will generate predictions using X.

Returns

Prediction intervals, keys are in the format {coverage}_lower or {coverage}_upper.

Return type

dict

Raises

MethodPropertyNotFoundError – If the estimator does not support Time Series Regression as a problem type.

static load(file_path)

Loads component at file path.

Parameters

file_path (str) – Location to load file.

Returns

ComponentBase object

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.

property parameters(self)

Returns the parameters which were used to initialize the component.

predict(self, X)

Make predictions using the fitted CatBoost classifier.

Parameters

X (pd.DataFrame) – Data of shape [n_samples, n_features].

Returns

Predicted values.

Return type

pd.Series

predict_proba(self, X)

Make prediction probabilities using the fitted CatBoost classifier.

Parameters

X (pd.DataFrame) – Data of shape [n_samples, n_features].

Returns

Predicted probability values.

Return type

pd.DataFrame

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.

update_parameters(self, update_dict, reset_fit=True)

Updates the parameter dictionary of the component.

Parameters

• update_dict (dict) – A dict of parameters to update.

• reset_fit (bool, optional) – If True, will set _is_fitted to False.