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)[source]¶

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
predict_uses_y	False
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.

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