et_classifier ========================================================================== .. py:module:: evalml.pipelines.components.estimators.classifiers.et_classifier .. autoapi-nested-parse:: Extra Trees Classifier. Module Contents --------------- Classes Summary ~~~~~~~~~~~~~~~ .. autoapisummary:: evalml.pipelines.components.estimators.classifiers.et_classifier.ExtraTreesClassifier Contents ~~~~~~~~~~~~~~~~~~~ .. py:class:: 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) Extra Trees Classifier. :param n_estimators: The number of trees in the forest. Defaults to 100. :type n_estimators: float :param max_features: 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". :type max_features: int, float or {"auto", "sqrt", "log2"} :param max_depth: The maximum depth of the tree. Defaults to 6. :type max_depth: int :param min_samples_split: 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. :type min_samples_split: int or float :param Defaults to 2.: :param min_weight_fraction_leaf: 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. :type min_weight_fraction_leaf: float :param n_jobs: Number of jobs to run in parallel. -1 uses all processes. Defaults to -1. :type n_jobs: int or None :param random_seed: Seed for the random number generator. Defaults to 0. :type random_seed: int **Attributes** .. list-table:: :widths: 15 85 :header-rows: 0 * - **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** .. autoapisummary:: :nosignatures: evalml.pipelines.components.estimators.classifiers.et_classifier.ExtraTreesClassifier.clone evalml.pipelines.components.estimators.classifiers.et_classifier.ExtraTreesClassifier.default_parameters evalml.pipelines.components.estimators.classifiers.et_classifier.ExtraTreesClassifier.describe evalml.pipelines.components.estimators.classifiers.et_classifier.ExtraTreesClassifier.feature_importance evalml.pipelines.components.estimators.classifiers.et_classifier.ExtraTreesClassifier.fit evalml.pipelines.components.estimators.classifiers.et_classifier.ExtraTreesClassifier.load evalml.pipelines.components.estimators.classifiers.et_classifier.ExtraTreesClassifier.needs_fitting evalml.pipelines.components.estimators.classifiers.et_classifier.ExtraTreesClassifier.parameters evalml.pipelines.components.estimators.classifiers.et_classifier.ExtraTreesClassifier.predict evalml.pipelines.components.estimators.classifiers.et_classifier.ExtraTreesClassifier.predict_proba evalml.pipelines.components.estimators.classifiers.et_classifier.ExtraTreesClassifier.save .. py:method:: 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. .. py:method:: 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. :rtype: dict .. py:method:: describe(self, print_name=False, return_dict=False) Describe a component and its parameters. :param print_name: whether to print name of component :type print_name: bool, optional :param return_dict: whether to return description as dictionary in the format {"name": name, "parameters": parameters} :type return_dict: bool, optional :returns: Returns dictionary if return_dict is True, else None. :rtype: None or dict .. py:method:: feature_importance(self) :property: Returns importance associated with each feature. :returns: Importance associated with each feature. :rtype: np.ndarray :raises MethodPropertyNotFoundError: If estimator does not have a feature_importance method or a component_obj that implements feature_importance. .. py:method:: fit(self, X, y=None) Fits estimator to data. :param X: The input training data of shape [n_samples, n_features]. :type X: pd.DataFrame :param y: The target training data of length [n_samples]. :type y: pd.Series, optional :returns: self .. py:method:: load(file_path) :staticmethod: Loads component at file path. :param file_path: Location to load file. :type file_path: str :returns: ComponentBase object .. py:method:: 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. .. py:method:: parameters(self) :property: Returns the parameters which were used to initialize the component. .. py:method:: predict(self, X) Make predictions using selected features. :param X: Data of shape [n_samples, n_features]. :type X: pd.DataFrame :returns: Predicted values. :rtype: pd.Series :raises MethodPropertyNotFoundError: If estimator does not have a predict method or a component_obj that implements predict. .. py:method:: predict_proba(self, X) Make probability estimates for labels. :param X: Features. :type X: pd.DataFrame :returns: Probability estimates. :rtype: pd.Series :raises MethodPropertyNotFoundError: If estimator does not have a predict_proba method or a component_obj that implements predict_proba. .. py:method:: save(self, file_path, pickle_protocol=cloudpickle.DEFAULT_PROTOCOL) Saves component at file path. :param file_path: Location to save file. :type file_path: str :param pickle_protocol: The pickle data stream format. :type pickle_protocol: int