et_classifier

Extra Trees Classifier.

Module Contents

Classes Summary

ExtraTreesClassifier

Extra Trees Classifier.

Contents

class evalml.pipelines.components.estimators.classifiers.et_classifier.ExtraTreesClassifier(n_estimators=100, max_features='auto', max_depth=6, min_samples_split=2, min_weight_fraction_leaf=0.0, n_jobs=- 1, random_seed=0, **kwargs)[source]

Extra Trees Classifier.

Parameters
  • n_estimators (float) – The number of trees in the forest. Defaults to 100.

  • max_features (int, float or {"auto", "sqrt", "log2"}) –

    The number of features to consider when looking for the best split:

    • If int, then consider max_features features at each split.

    • If float, then max_features is a fraction and int(max_features * n_features) features are considered at each split.

    • If “auto”, then max_features=sqrt(n_features).

    • If “sqrt”, then max_features=sqrt(n_features).

    • If “log2”, then max_features=log2(n_features).

    • If None, then max_features = n_features.

    The search for a split does not stop until at least one valid partition of the node samples is found, even if it requires to effectively inspect more than max_features features. Defaults to “auto”.

  • max_depth (int) – The maximum depth of the tree. Defaults to 6.

  • min_samples_split (int or float) –

    The minimum number of samples required to split an internal node:

    • If int, then consider min_samples_split as the minimum number.

    • If float, then min_samples_split is a fraction and ceil(min_samples_split * n_samples) are the minimum number of samples for each split.

  • to 2. (Defaults) –

  • min_weight_fraction_leaf (float) – The minimum weighted fraction of the sum total of weights (of all the input samples) required to be at a leaf node. Defaults to 0.0.

  • 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(10, 1000), “max_features”: [“auto”, “sqrt”, “log2”], “max_depth”: Integer(4, 10),}

model_family

ModelFamily.EXTRA_TREES

modifies_features

True

modifies_target

False

name

Extra Trees 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

Returns importance associated with each feature.

fit

Fits estimator to data.

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 selected features.

predict_proba

Make probability estimates for labels.

save

Saves component at file path.

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)

Returns importance associated with each feature.

Returns

Importance associated with each feature.

Return type

np.ndarray

Raises

MethodPropertyNotFoundError – If estimator does not have a feature_importance method or a component_obj that implements feature_importance.

fit(self, X, y=None)

Fits estimator to data.

Parameters
  • X (pd.DataFrame) – The input training data of shape [n_samples, n_features].

  • y (pd.Series, optional) – The target training data of length [n_samples].

Returns

self

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 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.

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.

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.