decision_tree_classifier

Decision Tree Classifier.

Module Contents

Classes Summary

DecisionTreeClassifier

Decision Tree Classifier.

Contents

class evalml.pipelines.components.estimators.classifiers.decision_tree_classifier.DecisionTreeClassifier(criterion='gini', max_features='auto', max_depth=6, min_samples_split=2, min_weight_fraction_leaf=0.0, random_seed=0, **kwargs)

Decision Tree Classifier.

Parameters

• criterion ({"gini", "entropy"}) – The function to measure the quality of a split. Supported criteria are “gini” for the Gini impurity and “entropy” for the information gain. Defaults to “gini”.

• 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.

  Defaults to 2.

• 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.

• random_seed (int) – Seed for the random number generator. Defaults to 0.

Attributes

hyperparameter_ranges	{ “criterion”: [“gini”, “entropy”], “max_features”: [“auto”, “sqrt”, “log2”], “max_depth”: Integer(4, 10),}
model_family	ModelFamily.DECISION_TREE
modifies_features	True
modifies_target	False
name	Decision Tree 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.