""" This module gathers tree-based methods, including decision, regression and randomized trees. Single and multi-output problems are both handled. """ # Authors: Gilles Louppe # Peter Prettenhofer # Brian Holt # Noel Dawe # Satrajit Gosh # Joly Arnaud # Fares Hedayati # Nelson Liu # # License: BSD 3 clause import numbers import warnings import copy from abc import ABCMeta from abc import abstractmethod from math import ceil from numbers import Integral, Real import numpy as np from scipy.sparse import issparse from ..base import BaseEstimator from ..base import ClassifierMixin from ..base import clone from ..base import RegressorMixin from ..base import is_classifier from ..base import MultiOutputMixin from ..utils import Bunch from ..utils import check_random_state from ..utils.validation import _check_sample_weight from ..utils import compute_sample_weight from ..utils.multiclass import check_classification_targets from ..utils.validation import check_is_fitted from ..utils._param_validation import Hidden, Interval, StrOptions from ._criterion import Criterion from ._splitter import Splitter from ._tree import DepthFirstTreeBuilder from ._tree import BestFirstTreeBuilder from ._tree import Tree from ._tree import _build_pruned_tree_ccp from ._tree import ccp_pruning_path from . import _tree, _splitter, _criterion __all__ = [ "DecisionTreeClassifier", "DecisionTreeRegressor", "ExtraTreeClassifier", "ExtraTreeRegressor", ] # ============================================================================= # Types and constants # ============================================================================= DTYPE = _tree.DTYPE DOUBLE = _tree.DOUBLE CRITERIA_CLF = { "gini": _criterion.Gini, "log_loss": _criterion.Entropy, "entropy": _criterion.Entropy, } CRITERIA_REG = { "squared_error": _criterion.MSE, "friedman_mse": _criterion.FriedmanMSE, "absolute_error": _criterion.MAE, "poisson": _criterion.Poisson, } DENSE_SPLITTERS = {"best": _splitter.BestSplitter, "random": _splitter.RandomSplitter} SPARSE_SPLITTERS = { "best": _splitter.BestSparseSplitter, "random": _splitter.RandomSparseSplitter, } # ============================================================================= # Base decision tree # ============================================================================= class BaseDecisionTree(MultiOutputMixin, BaseEstimator, metaclass=ABCMeta): """Base class for decision trees. Warning: This class should not be used directly. Use derived classes instead. """ _parameter_constraints: dict = { "splitter": [StrOptions({"best", "random"})], "max_depth": [Interval(Integral, 1, None, closed="left"), None], "min_samples_split": [ Interval(Integral, 2, None, closed="left"), Interval("real_not_int", 0.0, 1.0, closed="right"), ], "min_samples_leaf": [ Interval(Integral, 1, None, closed="left"), Interval("real_not_int", 0.0, 1.0, closed="neither"), ], "min_weight_fraction_leaf": [Interval(Real, 0.0, 0.5, closed="both")], "max_features": [ Interval(Integral, 1, None, closed="left"), Interval("real_not_int", 0.0, 1.0, closed="right"), StrOptions({"auto", "sqrt", "log2"}, deprecated={"auto"}), None, ], "random_state": ["random_state"], "max_leaf_nodes": [Interval(Integral, 2, None, closed="left"), None], "min_impurity_decrease": [Interval(Real, 0.0, None, closed="left")], "ccp_alpha": [Interval(Real, 0.0, None, closed="left")], } @abstractmethod def __init__( self, *, criterion, splitter, max_depth, min_samples_split, min_samples_leaf, min_weight_fraction_leaf, max_features, max_leaf_nodes, random_state, min_impurity_decrease, class_weight=None, ccp_alpha=0.0, ): self.criterion = criterion self.splitter = splitter self.max_depth = max_depth self.min_samples_split = min_samples_split self.min_samples_leaf = min_samples_leaf self.min_weight_fraction_leaf = min_weight_fraction_leaf self.max_features = max_features self.max_leaf_nodes = max_leaf_nodes self.random_state = random_state self.min_impurity_decrease = min_impurity_decrease self.class_weight = class_weight self.ccp_alpha = ccp_alpha def get_depth(self): """Return the depth of the decision tree. The depth of a tree is the maximum distance between the root and any leaf. Returns ------- self.tree_.max_depth : int The maximum depth of the tree. """ check_is_fitted(self) return self.tree_.max_depth def get_n_leaves(self): """Return the number of leaves of the decision tree. Returns ------- self.tree_.n_leaves : int Number of leaves. """ check_is_fitted(self) return self.tree_.n_leaves def fit(self, X, y, sample_weight=None, check_input=True): self._validate_params() random_state = check_random_state(self.random_state) if check_input: # Need to validate separately here. # We can't pass multi_output=True because that would allow y to be # csr. check_X_params = dict(dtype=DTYPE, accept_sparse="csc") check_y_params = dict(ensure_2d=False, dtype=None) X, y = self._validate_data( X, y, validate_separately=(check_X_params, check_y_params) ) if issparse(X): X.sort_indices() if X.indices.dtype != np.intc or X.indptr.dtype != np.intc: raise ValueError( "No support for np.int64 index based sparse matrices" ) if self.criterion == "poisson": if np.any(y < 0): raise ValueError( "Some value(s) of y are negative which is" " not allowed for Poisson regression." ) if np.sum(y) <= 0: raise ValueError( "Sum of y is not positive which is " "necessary for Poisson regression." ) # Determine output settings n_samples, self.n_features_in_ = X.shape is_classification = is_classifier(self) y = np.atleast_1d(y) expanded_class_weight = None if y.ndim == 1: # reshape is necessary to preserve the data contiguity against vs # [:, np.newaxis] that does not. y = np.reshape(y, (-1, 1)) self.n_outputs_ = y.shape[1] if is_classification: check_classification_targets(y) y = np.copy(y) self.classes_ = [] self.n_classes_ = [] if self.class_weight is not None: y_original = np.copy(y) y_encoded = np.zeros(y.shape, dtype=int) for k in range(self.n_outputs_): classes_k, y_encoded[:, k] = np.unique(y[:, k], return_inverse=True) self.classes_.append(classes_k) self.n_classes_.append(classes_k.shape[0]) y = y_encoded if self.class_weight is not None: expanded_class_weight = compute_sample_weight( self.class_weight, y_original ) self.n_classes_ = np.array(self.n_classes_, dtype=np.intp) if getattr(y, "dtype", None) != DOUBLE or not y.flags.contiguous: y = np.ascontiguousarray(y, dtype=DOUBLE) max_depth = np.iinfo(np.int32).max if self.max_depth is None else self.max_depth if isinstance(self.min_samples_leaf, numbers.Integral): min_samples_leaf = self.min_samples_leaf else: # float min_samples_leaf = int(ceil(self.min_samples_leaf * n_samples)) if isinstance(self.min_samples_split, numbers.Integral): min_samples_split = self.min_samples_split else: # float min_samples_split = int(ceil(self.min_samples_split * n_samples)) min_samples_split = max(2, min_samples_split) min_samples_split = max(min_samples_split, 2 * min_samples_leaf) if isinstance(self.max_features, str): if self.max_features == "auto": if is_classification: max_features = max(1, int(np.sqrt(self.n_features_in_))) warnings.warn( "`max_features='auto'` has been deprecated in 1.1 " "and will be removed in 1.3. To keep the past behaviour, " "explicitly set `max_features='sqrt'`.", FutureWarning, ) else: max_features = self.n_features_in_ warnings.warn( "`max_features='auto'` has been deprecated in 1.1 " "and will be removed in 1.3. To keep the past behaviour, " "explicitly set `max_features=1.0'`.", FutureWarning, ) elif self.max_features == "sqrt": max_features = max(1, int(np.sqrt(self.n_features_in_))) elif self.max_features == "log2": max_features = max(1, int(np.log2(self.n_features_in_))) elif self.max_features is None: max_features = self.n_features_in_ elif isinstance(self.max_features, numbers.Integral): max_features = self.max_features else: # float if self.max_features > 0.0: max_features = max(1, int(self.max_features * self.n_features_in_)) else: max_features = 0 self.max_features_ = max_features max_leaf_nodes = -1 if self.max_leaf_nodes is None else self.max_leaf_nodes if len(y) != n_samples: raise ValueError( "Number of labels=%d does not match number of samples=%d" % (len(y), n_samples) ) if sample_weight is not None: sample_weight = _check_sample_weight(sample_weight, X, DOUBLE) if expanded_class_weight is not None: if sample_weight is not None: sample_weight = sample_weight * expanded_class_weight else: sample_weight = expanded_class_weight # Set min_weight_leaf from min_weight_fraction_leaf if sample_weight is None: min_weight_leaf = self.min_weight_fraction_leaf * n_samples else: min_weight_leaf = self.min_weight_fraction_leaf * np.sum(sample_weight) # Build tree criterion = self.criterion if not isinstance(criterion, Criterion): if is_classification: criterion = CRITERIA_CLF[self.criterion]( self.n_outputs_, self.n_classes_ ) else: criterion = CRITERIA_REG[self.criterion](self.n_outputs_, n_samples) else: # Make a deepcopy in case the criterion has mutable attributes that # might be shared and modified concurrently during parallel fitting criterion = copy.deepcopy(criterion) SPLITTERS = SPARSE_SPLITTERS if issparse(X) else DENSE_SPLITTERS splitter = self.splitter if not isinstance(self.splitter, Splitter): splitter = SPLITTERS[self.splitter]( criterion, self.max_features_, min_samples_leaf, min_weight_leaf, random_state, ) if is_classifier(self): self.tree_ = Tree(self.n_features_in_, self.n_classes_, self.n_outputs_) else: self.tree_ = Tree( self.n_features_in_, # TODO: tree shouldn't need this in this case np.array([1] * self.n_outputs_, dtype=np.intp), self.n_outputs_, ) # Use BestFirst if max_leaf_nodes given; use DepthFirst otherwise if max_leaf_nodes < 0: builder = DepthFirstTreeBuilder( splitter, min_samples_split, min_samples_leaf, min_weight_leaf, max_depth, self.min_impurity_decrease, ) else: builder = BestFirstTreeBuilder( splitter, min_samples_split, min_samples_leaf, min_weight_leaf, max_depth, max_leaf_nodes, self.min_impurity_decrease, ) builder.build(self.tree_, X, y, sample_weight) if self.n_outputs_ == 1 and is_classifier(self): self.n_classes_ = self.n_classes_[0] self.classes_ = self.classes_[0] self._prune_tree() return self def _validate_X_predict(self, X, check_input): """Validate the training data on predict (probabilities).""" if check_input: X = self._validate_data(X, dtype=DTYPE, accept_sparse="csr", reset=False) if issparse(X) and ( X.indices.dtype != np.intc or X.indptr.dtype != np.intc ): raise ValueError("No support for np.int64 index based sparse matrices") else: # The number of features is checked regardless of `check_input` self._check_n_features(X, reset=False) return X def predict(self, X, check_input=True): """Predict class or regression value for X. For a classification model, the predicted class for each sample in X is returned. For a regression model, the predicted value based on X is returned. Parameters ---------- X : {array-like, sparse matrix} of shape (n_samples, n_features) The input samples. Internally, it will be converted to ``dtype=np.float32`` and if a sparse matrix is provided to a sparse ``csr_matrix``. check_input : bool, default=True Allow to bypass several input checking. Don't use this parameter unless you know what you're doing. Returns ------- y : array-like of shape (n_samples,) or (n_samples, n_outputs) The predicted classes, or the predict values. """ check_is_fitted(self) X = self._validate_X_predict(X, check_input) proba = self.tree_.predict(X) n_samples = X.shape[0] # Classification if is_classifier(self): if self.n_outputs_ == 1: return self.classes_.take(np.argmax(proba, axis=1), axis=0) else: class_type = self.classes_[0].dtype predictions = np.zeros((n_samples, self.n_outputs_), dtype=class_type) for k in range(self.n_outputs_): predictions[:, k] = self.classes_[k].take( np.argmax(proba[:, k], axis=1), axis=0 ) return predictions # Regression else: if self.n_outputs_ == 1: return proba[:, 0] else: return proba[:, :, 0] def apply(self, X, check_input=True): """Return the index of the leaf that each sample is predicted as. .. versionadded:: 0.17 Parameters ---------- X : {array-like, sparse matrix} of shape (n_samples, n_features) The input samples. Internally, it will be converted to ``dtype=np.float32`` and if a sparse matrix is provided to a sparse ``csr_matrix``. check_input : bool, default=True Allow to bypass several input checking. Don't use this parameter unless you know what you're doing. Returns ------- X_leaves : array-like of shape (n_samples,) For each datapoint x in X, return the index of the leaf x ends up in. Leaves are numbered within ``[0; self.tree_.node_count)``, possibly with gaps in the numbering. """ check_is_fitted(self) X = self._validate_X_predict(X, check_input) return self.tree_.apply(X) def decision_path(self, X, check_input=True): """Return the decision path in the tree. .. versionadded:: 0.18 Parameters ---------- X : {array-like, sparse matrix} of shape (n_samples, n_features) The input samples. Internally, it will be converted to ``dtype=np.float32`` and if a sparse matrix is provided to a sparse ``csr_matrix``. check_input : bool, default=True Allow to bypass several input checking. Don't use this parameter unless you know what you're doing. Returns ------- indicator : sparse matrix of shape (n_samples, n_nodes) Return a node indicator CSR matrix where non zero elements indicates that the samples goes through the nodes. """ X = self._validate_X_predict(X, check_input) return self.tree_.decision_path(X) def _prune_tree(self): """Prune tree using Minimal Cost-Complexity Pruning.""" check_is_fitted(self) if self.ccp_alpha == 0.0: return # build pruned tree if is_classifier(self): n_classes = np.atleast_1d(self.n_classes_) pruned_tree = Tree(self.n_features_in_, n_classes, self.n_outputs_) else: pruned_tree = Tree( self.n_features_in_, # TODO: the tree shouldn't need this param np.array([1] * self.n_outputs_, dtype=np.intp), self.n_outputs_, ) _build_pruned_tree_ccp(pruned_tree, self.tree_, self.ccp_alpha) self.tree_ = pruned_tree def cost_complexity_pruning_path(self, X, y, sample_weight=None): """Compute the pruning path during Minimal Cost-Complexity Pruning. See :ref:`minimal_cost_complexity_pruning` for details on the pruning process. Parameters ---------- X : {array-like, sparse matrix} of shape (n_samples, n_features) The training input samples. Internally, it will be converted to ``dtype=np.float32`` and if a sparse matrix is provided to a sparse ``csc_matrix``. y : array-like of shape (n_samples,) or (n_samples, n_outputs) The target values (class labels) as integers or strings. sample_weight : array-like of shape (n_samples,), default=None Sample weights. If None, then samples are equally weighted. Splits that would create child nodes with net zero or negative weight are ignored while searching for a split in each node. Splits are also ignored if they would result in any single class carrying a negative weight in either child node. Returns ------- ccp_path : :class:`~sklearn.utils.Bunch` Dictionary-like object, with the following attributes. ccp_alphas : ndarray Effective alphas of subtree during pruning. impurities : ndarray Sum of the impurities of the subtree leaves for the corresponding alpha value in ``ccp_alphas``. """ est = clone(self).set_params(ccp_alpha=0.0) est.fit(X, y, sample_weight=sample_weight) return Bunch(**ccp_pruning_path(est.tree_)) @property def feature_importances_(self): """Return the feature importances. The importance of a feature is computed as the (normalized) total reduction of the criterion brought by that feature. It is also known as the Gini importance. Warning: impurity-based feature importances can be misleading for high cardinality features (many unique values). See :func:`sklearn.inspection.permutation_importance` as an alternative. Returns ------- feature_importances_ : ndarray of shape (n_features,) Normalized total reduction of criteria by feature (Gini importance). """ check_is_fitted(self) return self.tree_.compute_feature_importances() # ============================================================================= # Public estimators # ============================================================================= class DecisionTreeClassifier(ClassifierMixin, BaseDecisionTree): """A decision tree classifier. Read more in the :ref:`User Guide `. Parameters ---------- criterion : {"gini", "entropy", "log_loss"}, default="gini" The function to measure the quality of a split. Supported criteria are "gini" for the Gini impurity and "log_loss" and "entropy" both for the Shannon information gain, see :ref:`tree_mathematical_formulation`. splitter : {"best", "random"}, default="best" The strategy used to choose the split at each node. Supported strategies are "best" to choose the best split and "random" to choose the best random split. max_depth : int, default=None The maximum depth of the tree. If None, then nodes are expanded until all leaves are pure or until all leaves contain less than min_samples_split samples. min_samples_split : int or float, default=2 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. .. versionchanged:: 0.18 Added float values for fractions. min_samples_leaf : int or float, default=1 The minimum number of samples required to be at a leaf node. A split point at any depth will only be considered if it leaves at least ``min_samples_leaf`` training samples in each of the left and right branches. This may have the effect of smoothing the model, especially in regression. - If int, then consider `min_samples_leaf` as the minimum number. - If float, then `min_samples_leaf` is a fraction and `ceil(min_samples_leaf * n_samples)` are the minimum number of samples for each node. .. versionchanged:: 0.18 Added float values for fractions. min_weight_fraction_leaf : float, default=0.0 The minimum weighted fraction of the sum total of weights (of all the input samples) required to be at a leaf node. Samples have equal weight when sample_weight is not provided. max_features : int, float or {"auto", "sqrt", "log2"}, default=None 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 `max(1, int(max_features * n_features_in_))` 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`. .. deprecated:: 1.1 The `"auto"` option was deprecated in 1.1 and will be removed in 1.3. Note: 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. random_state : int, RandomState instance or None, default=None Controls the randomness of the estimator. The features are always randomly permuted at each split, even if ``splitter`` is set to ``"best"``. When ``max_features < n_features``, the algorithm will select ``max_features`` at random at each split before finding the best split among them. But the best found split may vary across different runs, even if ``max_features=n_features``. That is the case, if the improvement of the criterion is identical for several splits and one split has to be selected at random. To obtain a deterministic behaviour during fitting, ``random_state`` has to be fixed to an integer. See :term:`Glossary ` for details. max_leaf_nodes : int, default=None Grow a tree with ``max_leaf_nodes`` in best-first fashion. Best nodes are defined as relative reduction in impurity. If None then unlimited number of leaf nodes. min_impurity_decrease : float, default=0.0 A node will be split if this split induces a decrease of the impurity greater than or equal to this value. The weighted impurity decrease equation is the following:: N_t / N * (impurity - N_t_R / N_t * right_impurity - N_t_L / N_t * left_impurity) where ``N`` is the total number of samples, ``N_t`` is the number of samples at the current node, ``N_t_L`` is the number of samples in the left child, and ``N_t_R`` is the number of samples in the right child. ``N``, ``N_t``, ``N_t_R`` and ``N_t_L`` all refer to the weighted sum, if ``sample_weight`` is passed. .. versionadded:: 0.19 class_weight : dict, list of dict or "balanced", default=None Weights associated with classes in the form ``{class_label: weight}``. If None, all classes are supposed to have weight one. For multi-output problems, a list of dicts can be provided in the same order as the columns of y. Note that for multioutput (including multilabel) weights should be defined for each class of every column in its own dict. For example, for four-class multilabel classification weights should be [{0: 1, 1: 1}, {0: 1, 1: 5}, {0: 1, 1: 1}, {0: 1, 1: 1}] instead of [{1:1}, {2:5}, {3:1}, {4:1}]. The "balanced" mode uses the values of y to automatically adjust weights inversely proportional to class frequencies in the input data as ``n_samples / (n_classes * np.bincount(y))`` For multi-output, the weights of each column of y will be multiplied. Note that these weights will be multiplied with sample_weight (passed through the fit method) if sample_weight is specified. ccp_alpha : non-negative float, default=0.0 Complexity parameter used for Minimal Cost-Complexity Pruning. The subtree with the largest cost complexity that is smaller than ``ccp_alpha`` will be chosen. By default, no pruning is performed. See :ref:`minimal_cost_complexity_pruning` for details. .. versionadded:: 0.22 Attributes ---------- classes_ : ndarray of shape (n_classes,) or list of ndarray The classes labels (single output problem), or a list of arrays of class labels (multi-output problem). feature_importances_ : ndarray of shape (n_features,) The impurity-based feature importances. The higher, the more important the feature. The importance of a feature is computed as the (normalized) total reduction of the criterion brought by that feature. It is also known as the Gini importance [4]_. Warning: impurity-based feature importances can be misleading for high cardinality features (many unique values). See :func:`sklearn.inspection.permutation_importance` as an alternative. max_features_ : int The inferred value of max_features. n_classes_ : int or list of int The number of classes (for single output problems), or a list containing the number of classes for each output (for multi-output problems). n_features_in_ : int Number of features seen during :term:`fit`. .. versionadded:: 0.24 feature_names_in_ : ndarray of shape (`n_features_in_`,) Names of features seen during :term:`fit`. Defined only when `X` has feature names that are all strings. .. versionadded:: 1.0 n_outputs_ : int The number of outputs when ``fit`` is performed. tree_ : Tree instance The underlying Tree object. Please refer to ``help(sklearn.tree._tree.Tree)`` for attributes of Tree object and :ref:`sphx_glr_auto_examples_tree_plot_unveil_tree_structure.py` for basic usage of these attributes. See Also -------- DecisionTreeRegressor : A decision tree regressor. Notes ----- The default values for the parameters controlling the size of the trees (e.g. ``max_depth``, ``min_samples_leaf``, etc.) lead to fully grown and unpruned trees which can potentially be very large on some data sets. To reduce memory consumption, the complexity and size of the trees should be controlled by setting those parameter values. The :meth:`predict` method operates using the :func:`numpy.argmax` function on the outputs of :meth:`predict_proba`. This means that in case the highest predicted probabilities are tied, the classifier will predict the tied class with the lowest index in :term:`classes_`. References ---------- .. [1] https://en.wikipedia.org/wiki/Decision_tree_learning .. [2] L. Breiman, J. Friedman, R. Olshen, and C. Stone, "Classification and Regression Trees", Wadsworth, Belmont, CA, 1984. .. [3] T. Hastie, R. Tibshirani and J. Friedman. "Elements of Statistical Learning", Springer, 2009. .. [4] L. Breiman, and A. Cutler, "Random Forests", https://www.stat.berkeley.edu/~breiman/RandomForests/cc_home.htm Examples -------- >>> from sklearn.datasets import load_iris >>> from sklearn.model_selection import cross_val_score >>> from sklearn.tree import DecisionTreeClassifier >>> clf = DecisionTreeClassifier(random_state=0) >>> iris = load_iris() >>> cross_val_score(clf, iris.data, iris.target, cv=10) ... # doctest: +SKIP ... array([ 1. , 0.93..., 0.86..., 0.93..., 0.93..., 0.93..., 0.93..., 1. , 0.93..., 1. ]) """ _parameter_constraints: dict = { **BaseDecisionTree._parameter_constraints, "criterion": [StrOptions({"gini", "entropy", "log_loss"}), Hidden(Criterion)], "class_weight": [dict, list, StrOptions({"balanced"}), None], } def __init__( self, *, criterion="gini", splitter="best", max_depth=None, min_samples_split=2, min_samples_leaf=1, min_weight_fraction_leaf=0.0, max_features=None, random_state=None, max_leaf_nodes=None, min_impurity_decrease=0.0, class_weight=None, ccp_alpha=0.0, ): super().__init__( criterion=criterion, splitter=splitter, max_depth=max_depth, min_samples_split=min_samples_split, min_samples_leaf=min_samples_leaf, min_weight_fraction_leaf=min_weight_fraction_leaf, max_features=max_features, max_leaf_nodes=max_leaf_nodes, class_weight=class_weight, random_state=random_state, min_impurity_decrease=min_impurity_decrease, ccp_alpha=ccp_alpha, ) def fit(self, X, y, sample_weight=None, check_input=True): """Build a decision tree classifier from the training set (X, y). Parameters ---------- X : {array-like, sparse matrix} of shape (n_samples, n_features) The training input samples. Internally, it will be converted to ``dtype=np.float32`` and if a sparse matrix is provided to a sparse ``csc_matrix``. y : array-like of shape (n_samples,) or (n_samples, n_outputs) The target values (class labels) as integers or strings. sample_weight : array-like of shape (n_samples,), default=None Sample weights. If None, then samples are equally weighted. Splits that would create child nodes with net zero or negative weight are ignored while searching for a split in each node. Splits are also ignored if they would result in any single class carrying a negative weight in either child node. check_input : bool, default=True Allow to bypass several input checking. Don't use this parameter unless you know what you're doing. Returns ------- self : DecisionTreeClassifier Fitted estimator. """ super().fit( X, y, sample_weight=sample_weight, check_input=check_input, ) return self def predict_proba(self, X, check_input=True): """Predict class probabilities of the input samples X. The predicted class probability is the fraction of samples of the same class in a leaf. Parameters ---------- X : {array-like, sparse matrix} of shape (n_samples, n_features) The input samples. Internally, it will be converted to ``dtype=np.float32`` and if a sparse matrix is provided to a sparse ``csr_matrix``. check_input : bool, default=True Allow to bypass several input checking. Don't use this parameter unless you know what you're doing. Returns ------- proba : ndarray of shape (n_samples, n_classes) or list of n_outputs \ such arrays if n_outputs > 1 The class probabilities of the input samples. The order of the classes corresponds to that in the attribute :term:`classes_`. """ check_is_fitted(self) X = self._validate_X_predict(X, check_input) proba = self.tree_.predict(X) if self.n_outputs_ == 1: proba = proba[:, : self.n_classes_] normalizer = proba.sum(axis=1)[:, np.newaxis] normalizer[normalizer == 0.0] = 1.0 proba /= normalizer return proba else: all_proba = [] for k in range(self.n_outputs_): proba_k = proba[:, k, : self.n_classes_[k]] normalizer = proba_k.sum(axis=1)[:, np.newaxis] normalizer[normalizer == 0.0] = 1.0 proba_k /= normalizer all_proba.append(proba_k) return all_proba def predict_log_proba(self, X): """Predict class log-probabilities of the input samples X. Parameters ---------- X : {array-like, sparse matrix} of shape (n_samples, n_features) The input samples. Internally, it will be converted to ``dtype=np.float32`` and if a sparse matrix is provided to a sparse ``csr_matrix``. Returns ------- proba : ndarray of shape (n_samples, n_classes) or list of n_outputs \ such arrays if n_outputs > 1 The class log-probabilities of the input samples. The order of the classes corresponds to that in the attribute :term:`classes_`. """ proba = self.predict_proba(X) if self.n_outputs_ == 1: return np.log(proba) else: for k in range(self.n_outputs_): proba[k] = np.log(proba[k]) return proba def _more_tags(self): return {"multilabel": True} class DecisionTreeRegressor(RegressorMixin, BaseDecisionTree): """A decision tree regressor. Read more in the :ref:`User Guide `. Parameters ---------- criterion : {"squared_error", "friedman_mse", "absolute_error", \ "poisson"}, default="squared_error" The function to measure the quality of a split. Supported criteria are "squared_error" for the mean squared error, which is equal to variance reduction as feature selection criterion and minimizes the L2 loss using the mean of each terminal node, "friedman_mse", which uses mean squared error with Friedman's improvement score for potential splits, "absolute_error" for the mean absolute error, which minimizes the L1 loss using the median of each terminal node, and "poisson" which uses reduction in Poisson deviance to find splits. .. versionadded:: 0.18 Mean Absolute Error (MAE) criterion. .. versionadded:: 0.24 Poisson deviance criterion. splitter : {"best", "random"}, default="best" The strategy used to choose the split at each node. Supported strategies are "best" to choose the best split and "random" to choose the best random split. max_depth : int, default=None The maximum depth of the tree. If None, then nodes are expanded until all leaves are pure or until all leaves contain less than min_samples_split samples. min_samples_split : int or float, default=2 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. .. versionchanged:: 0.18 Added float values for fractions. min_samples_leaf : int or float, default=1 The minimum number of samples required to be at a leaf node. A split point at any depth will only be considered if it leaves at least ``min_samples_leaf`` training samples in each of the left and right branches. This may have the effect of smoothing the model, especially in regression. - If int, then consider `min_samples_leaf` as the minimum number. - If float, then `min_samples_leaf` is a fraction and `ceil(min_samples_leaf * n_samples)` are the minimum number of samples for each node. .. versionchanged:: 0.18 Added float values for fractions. min_weight_fraction_leaf : float, default=0.0 The minimum weighted fraction of the sum total of weights (of all the input samples) required to be at a leaf node. Samples have equal weight when sample_weight is not provided. max_features : int, float or {"auto", "sqrt", "log2"}, default=None 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 `max(1, int(max_features * n_features_in_))` features are considered at each split. - If "auto", then `max_features=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`. .. deprecated:: 1.1 The `"auto"` option was deprecated in 1.1 and will be removed in 1.3. Note: 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. random_state : int, RandomState instance or None, default=None Controls the randomness of the estimator. The features are always randomly permuted at each split, even if ``splitter`` is set to ``"best"``. When ``max_features < n_features``, the algorithm will select ``max_features`` at random at each split before finding the best split among them. But the best found split may vary across different runs, even if ``max_features=n_features``. That is the case, if the improvement of the criterion is identical for several splits and one split has to be selected at random. To obtain a deterministic behaviour during fitting, ``random_state`` has to be fixed to an integer. See :term:`Glossary ` for details. max_leaf_nodes : int, default=None Grow a tree with ``max_leaf_nodes`` in best-first fashion. Best nodes are defined as relative reduction in impurity. If None then unlimited number of leaf nodes. min_impurity_decrease : float, default=0.0 A node will be split if this split induces a decrease of the impurity greater than or equal to this value. The weighted impurity decrease equation is the following:: N_t / N * (impurity - N_t_R / N_t * right_impurity - N_t_L / N_t * left_impurity) where ``N`` is the total number of samples, ``N_t`` is the number of samples at the current node, ``N_t_L`` is the number of samples in the left child, and ``N_t_R`` is the number of samples in the right child. ``N``, ``N_t``, ``N_t_R`` and ``N_t_L`` all refer to the weighted sum, if ``sample_weight`` is passed. .. versionadded:: 0.19 ccp_alpha : non-negative float, default=0.0 Complexity parameter used for Minimal Cost-Complexity Pruning. The subtree with the largest cost complexity that is smaller than ``ccp_alpha`` will be chosen. By default, no pruning is performed. See :ref:`minimal_cost_complexity_pruning` for details. .. versionadded:: 0.22 Attributes ---------- feature_importances_ : ndarray of shape (n_features,) The feature importances. The higher, the more important the feature. The importance of a feature is computed as the (normalized) total reduction of the criterion brought by that feature. It is also known as the Gini importance [4]_. Warning: impurity-based feature importances can be misleading for high cardinality features (many unique values). See :func:`sklearn.inspection.permutation_importance` as an alternative. max_features_ : int The inferred value of max_features. n_features_in_ : int Number of features seen during :term:`fit`. .. versionadded:: 0.24 feature_names_in_ : ndarray of shape (`n_features_in_`,) Names of features seen during :term:`fit`. Defined only when `X` has feature names that are all strings. .. versionadded:: 1.0 n_outputs_ : int The number of outputs when ``fit`` is performed. tree_ : Tree instance The underlying Tree object. Please refer to ``help(sklearn.tree._tree.Tree)`` for attributes of Tree object and :ref:`sphx_glr_auto_examples_tree_plot_unveil_tree_structure.py` for basic usage of these attributes. See Also -------- DecisionTreeClassifier : A decision tree classifier. Notes ----- The default values for the parameters controlling the size of the trees (e.g. ``max_depth``, ``min_samples_leaf``, etc.) lead to fully grown and unpruned trees which can potentially be very large on some data sets. To reduce memory consumption, the complexity and size of the trees should be controlled by setting those parameter values. References ---------- .. [1] https://en.wikipedia.org/wiki/Decision_tree_learning .. [2] L. Breiman, J. Friedman, R. Olshen, and C. Stone, "Classification and Regression Trees", Wadsworth, Belmont, CA, 1984. .. [3] T. Hastie, R. Tibshirani and J. Friedman. "Elements of Statistical Learning", Springer, 2009. .. [4] L. Breiman, and A. Cutler, "Random Forests", https://www.stat.berkeley.edu/~breiman/RandomForests/cc_home.htm Examples -------- >>> from sklearn.datasets import load_diabetes >>> from sklearn.model_selection import cross_val_score >>> from sklearn.tree import DecisionTreeRegressor >>> X, y = load_diabetes(return_X_y=True) >>> regressor = DecisionTreeRegressor(random_state=0) >>> cross_val_score(regressor, X, y, cv=10) ... # doctest: +SKIP ... array([-0.39..., -0.46..., 0.02..., 0.06..., -0.50..., 0.16..., 0.11..., -0.73..., -0.30..., -0.00...]) """ _parameter_constraints: dict = { **BaseDecisionTree._parameter_constraints, "criterion": [ StrOptions({"squared_error", "friedman_mse", "absolute_error", "poisson"}), Hidden(Criterion), ], } def __init__( self, *, criterion="squared_error", splitter="best", max_depth=None, min_samples_split=2, min_samples_leaf=1, min_weight_fraction_leaf=0.0, max_features=None, random_state=None, max_leaf_nodes=None, min_impurity_decrease=0.0, ccp_alpha=0.0, ): super().__init__( criterion=criterion, splitter=splitter, max_depth=max_depth, min_samples_split=min_samples_split, min_samples_leaf=min_samples_leaf, min_weight_fraction_leaf=min_weight_fraction_leaf, max_features=max_features, max_leaf_nodes=max_leaf_nodes, random_state=random_state, min_impurity_decrease=min_impurity_decrease, ccp_alpha=ccp_alpha, ) def fit(self, X, y, sample_weight=None, check_input=True): """Build a decision tree regressor from the training set (X, y). Parameters ---------- X : {array-like, sparse matrix} of shape (n_samples, n_features) The training input samples. Internally, it will be converted to ``dtype=np.float32`` and if a sparse matrix is provided to a sparse ``csc_matrix``. y : array-like of shape (n_samples,) or (n_samples, n_outputs) The target values (real numbers). Use ``dtype=np.float64`` and ``order='C'`` for maximum efficiency. sample_weight : array-like of shape (n_samples,), default=None Sample weights. If None, then samples are equally weighted. Splits that would create child nodes with net zero or negative weight are ignored while searching for a split in each node. check_input : bool, default=True Allow to bypass several input checking. Don't use this parameter unless you know what you're doing. Returns ------- self : DecisionTreeRegressor Fitted estimator. """ super().fit( X, y, sample_weight=sample_weight, check_input=check_input, ) return self def _compute_partial_dependence_recursion(self, grid, target_features): """Fast partial dependence computation. Parameters ---------- grid : ndarray of shape (n_samples, n_target_features) The grid points on which the partial dependence should be evaluated. target_features : ndarray of shape (n_target_features) The set of target features for which the partial dependence should be evaluated. Returns ------- averaged_predictions : ndarray of shape (n_samples,) The value of the partial dependence function on each grid point. """ grid = np.asarray(grid, dtype=DTYPE, order="C") averaged_predictions = np.zeros( shape=grid.shape[0], dtype=np.float64, order="C" ) self.tree_.compute_partial_dependence( grid, target_features, averaged_predictions ) return averaged_predictions class ExtraTreeClassifier(DecisionTreeClassifier): """An extremely randomized tree classifier. Extra-trees differ from classic decision trees in the way they are built. When looking for the best split to separate the samples of a node into two groups, random splits are drawn for each of the `max_features` randomly selected features and the best split among those is chosen. When `max_features` is set 1, this amounts to building a totally random decision tree. Warning: Extra-trees should only be used within ensemble methods. Read more in the :ref:`User Guide `. Parameters ---------- criterion : {"gini", "entropy", "log_loss"}, default="gini" The function to measure the quality of a split. Supported criteria are "gini" for the Gini impurity and "log_loss" and "entropy" both for the Shannon information gain, see :ref:`tree_mathematical_formulation`. splitter : {"random", "best"}, default="random" The strategy used to choose the split at each node. Supported strategies are "best" to choose the best split and "random" to choose the best random split. max_depth : int, default=None The maximum depth of the tree. If None, then nodes are expanded until all leaves are pure or until all leaves contain less than min_samples_split samples. min_samples_split : int or float, default=2 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. .. versionchanged:: 0.18 Added float values for fractions. min_samples_leaf : int or float, default=1 The minimum number of samples required to be at a leaf node. A split point at any depth will only be considered if it leaves at least ``min_samples_leaf`` training samples in each of the left and right branches. This may have the effect of smoothing the model, especially in regression. - If int, then consider `min_samples_leaf` as the minimum number. - If float, then `min_samples_leaf` is a fraction and `ceil(min_samples_leaf * n_samples)` are the minimum number of samples for each node. .. versionchanged:: 0.18 Added float values for fractions. min_weight_fraction_leaf : float, default=0.0 The minimum weighted fraction of the sum total of weights (of all the input samples) required to be at a leaf node. Samples have equal weight when sample_weight is not provided. max_features : int, float, {"auto", "sqrt", "log2"} or None, default="sqrt" 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 `max(1, int(max_features * n_features_in_))` 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`. .. versionchanged:: 1.1 The default of `max_features` changed from `"auto"` to `"sqrt"`. .. deprecated:: 1.1 The `"auto"` option was deprecated in 1.1 and will be removed in 1.3. Note: 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. random_state : int, RandomState instance or None, default=None Used to pick randomly the `max_features` used at each split. See :term:`Glossary ` for details. max_leaf_nodes : int, default=None Grow a tree with ``max_leaf_nodes`` in best-first fashion. Best nodes are defined as relative reduction in impurity. If None then unlimited number of leaf nodes. min_impurity_decrease : float, default=0.0 A node will be split if this split induces a decrease of the impurity greater than or equal to this value. The weighted impurity decrease equation is the following:: N_t / N * (impurity - N_t_R / N_t * right_impurity - N_t_L / N_t * left_impurity) where ``N`` is the total number of samples, ``N_t`` is the number of samples at the current node, ``N_t_L`` is the number of samples in the left child, and ``N_t_R`` is the number of samples in the right child. ``N``, ``N_t``, ``N_t_R`` and ``N_t_L`` all refer to the weighted sum, if ``sample_weight`` is passed. .. versionadded:: 0.19 class_weight : dict, list of dict or "balanced", default=None Weights associated with classes in the form ``{class_label: weight}``. If None, all classes are supposed to have weight one. For multi-output problems, a list of dicts can be provided in the same order as the columns of y. Note that for multioutput (including multilabel) weights should be defined for each class of every column in its own dict. For example, for four-class multilabel classification weights should be [{0: 1, 1: 1}, {0: 1, 1: 5}, {0: 1, 1: 1}, {0: 1, 1: 1}] instead of [{1:1}, {2:5}, {3:1}, {4:1}]. The "balanced" mode uses the values of y to automatically adjust weights inversely proportional to class frequencies in the input data as ``n_samples / (n_classes * np.bincount(y))`` For multi-output, the weights of each column of y will be multiplied. Note that these weights will be multiplied with sample_weight (passed through the fit method) if sample_weight is specified. ccp_alpha : non-negative float, default=0.0 Complexity parameter used for Minimal Cost-Complexity Pruning. The subtree with the largest cost complexity that is smaller than ``ccp_alpha`` will be chosen. By default, no pruning is performed. See :ref:`minimal_cost_complexity_pruning` for details. .. versionadded:: 0.22 Attributes ---------- classes_ : ndarray of shape (n_classes,) or list of ndarray The classes labels (single output problem), or a list of arrays of class labels (multi-output problem). max_features_ : int The inferred value of max_features. n_classes_ : int or list of int The number of classes (for single output problems), or a list containing the number of classes for each output (for multi-output problems). feature_importances_ : ndarray of shape (n_features,) The impurity-based feature importances. The higher, the more important the feature. The importance of a feature is computed as the (normalized) total reduction of the criterion brought by that feature. It is also known as the Gini importance. Warning: impurity-based feature importances can be misleading for high cardinality features (many unique values). See :func:`sklearn.inspection.permutation_importance` as an alternative. n_features_in_ : int Number of features seen during :term:`fit`. .. versionadded:: 0.24 feature_names_in_ : ndarray of shape (`n_features_in_`,) Names of features seen during :term:`fit`. Defined only when `X` has feature names that are all strings. .. versionadded:: 1.0 n_outputs_ : int The number of outputs when ``fit`` is performed. tree_ : Tree instance The underlying Tree object. Please refer to ``help(sklearn.tree._tree.Tree)`` for attributes of Tree object and :ref:`sphx_glr_auto_examples_tree_plot_unveil_tree_structure.py` for basic usage of these attributes. See Also -------- ExtraTreeRegressor : An extremely randomized tree regressor. sklearn.ensemble.ExtraTreesClassifier : An extra-trees classifier. sklearn.ensemble.ExtraTreesRegressor : An extra-trees regressor. sklearn.ensemble.RandomForestClassifier : A random forest classifier. sklearn.ensemble.RandomForestRegressor : A random forest regressor. sklearn.ensemble.RandomTreesEmbedding : An ensemble of totally random trees. Notes ----- The default values for the parameters controlling the size of the trees (e.g. ``max_depth``, ``min_samples_leaf``, etc.) lead to fully grown and unpruned trees which can potentially be very large on some data sets. To reduce memory consumption, the complexity and size of the trees should be controlled by setting those parameter values. References ---------- .. [1] P. Geurts, D. Ernst., and L. Wehenkel, "Extremely randomized trees", Machine Learning, 63(1), 3-42, 2006. Examples -------- >>> from sklearn.datasets import load_iris >>> from sklearn.model_selection import train_test_split >>> from sklearn.ensemble import BaggingClassifier >>> from sklearn.tree import ExtraTreeClassifier >>> X, y = load_iris(return_X_y=True) >>> X_train, X_test, y_train, y_test = train_test_split( ... X, y, random_state=0) >>> extra_tree = ExtraTreeClassifier(random_state=0) >>> cls = BaggingClassifier(extra_tree, random_state=0).fit( ... X_train, y_train) >>> cls.score(X_test, y_test) 0.8947... """ def __init__( self, *, criterion="gini", splitter="random", max_depth=None, min_samples_split=2, min_samples_leaf=1, min_weight_fraction_leaf=0.0, max_features="sqrt", random_state=None, max_leaf_nodes=None, min_impurity_decrease=0.0, class_weight=None, ccp_alpha=0.0, ): super().__init__( criterion=criterion, splitter=splitter, max_depth=max_depth, min_samples_split=min_samples_split, min_samples_leaf=min_samples_leaf, min_weight_fraction_leaf=min_weight_fraction_leaf, max_features=max_features, max_leaf_nodes=max_leaf_nodes, class_weight=class_weight, min_impurity_decrease=min_impurity_decrease, random_state=random_state, ccp_alpha=ccp_alpha, ) class ExtraTreeRegressor(DecisionTreeRegressor): """An extremely randomized tree regressor. Extra-trees differ from classic decision trees in the way they are built. When looking for the best split to separate the samples of a node into two groups, random splits are drawn for each of the `max_features` randomly selected features and the best split among those is chosen. When `max_features` is set 1, this amounts to building a totally random decision tree. Warning: Extra-trees should only be used within ensemble methods. Read more in the :ref:`User Guide `. Parameters ---------- criterion : {"squared_error", "friedman_mse", "absolute_error", "poisson"}, \ default="squared_error" The function to measure the quality of a split. Supported criteria are "squared_error" for the mean squared error, which is equal to variance reduction as feature selection criterion and minimizes the L2 loss using the mean of each terminal node, "friedman_mse", which uses mean squared error with Friedman's improvement score for potential splits, "absolute_error" for the mean absolute error, which minimizes the L1 loss using the median of each terminal node, and "poisson" which uses reduction in Poisson deviance to find splits. .. versionadded:: 0.18 Mean Absolute Error (MAE) criterion. .. versionadded:: 0.24 Poisson deviance criterion. splitter : {"random", "best"}, default="random" The strategy used to choose the split at each node. Supported strategies are "best" to choose the best split and "random" to choose the best random split. max_depth : int, default=None The maximum depth of the tree. If None, then nodes are expanded until all leaves are pure or until all leaves contain less than min_samples_split samples. min_samples_split : int or float, default=2 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. .. versionchanged:: 0.18 Added float values for fractions. min_samples_leaf : int or float, default=1 The minimum number of samples required to be at a leaf node. A split point at any depth will only be considered if it leaves at least ``min_samples_leaf`` training samples in each of the left and right branches. This may have the effect of smoothing the model, especially in regression. - If int, then consider `min_samples_leaf` as the minimum number. - If float, then `min_samples_leaf` is a fraction and `ceil(min_samples_leaf * n_samples)` are the minimum number of samples for each node. .. versionchanged:: 0.18 Added float values for fractions. min_weight_fraction_leaf : float, default=0.0 The minimum weighted fraction of the sum total of weights (of all the input samples) required to be at a leaf node. Samples have equal weight when sample_weight is not provided. max_features : int, float, {"auto", "sqrt", "log2"} or None, default=1.0 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 `max(1, int(max_features * n_features_in_))` features are considered at each split. - If "auto", then `max_features=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`. .. versionchanged:: 1.1 The default of `max_features` changed from `"auto"` to `1.0`. .. deprecated:: 1.1 The `"auto"` option was deprecated in 1.1 and will be removed in 1.3. Note: 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. random_state : int, RandomState instance or None, default=None Used to pick randomly the `max_features` used at each split. See :term:`Glossary ` for details. min_impurity_decrease : float, default=0.0 A node will be split if this split induces a decrease of the impurity greater than or equal to this value. The weighted impurity decrease equation is the following:: N_t / N * (impurity - N_t_R / N_t * right_impurity - N_t_L / N_t * left_impurity) where ``N`` is the total number of samples, ``N_t`` is the number of samples at the current node, ``N_t_L`` is the number of samples in the left child, and ``N_t_R`` is the number of samples in the right child. ``N``, ``N_t``, ``N_t_R`` and ``N_t_L`` all refer to the weighted sum, if ``sample_weight`` is passed. .. versionadded:: 0.19 max_leaf_nodes : int, default=None Grow a tree with ``max_leaf_nodes`` in best-first fashion. Best nodes are defined as relative reduction in impurity. If None then unlimited number of leaf nodes. ccp_alpha : non-negative float, default=0.0 Complexity parameter used for Minimal Cost-Complexity Pruning. The subtree with the largest cost complexity that is smaller than ``ccp_alpha`` will be chosen. By default, no pruning is performed. See :ref:`minimal_cost_complexity_pruning` for details. .. versionadded:: 0.22 Attributes ---------- max_features_ : int The inferred value of max_features. n_features_in_ : int Number of features seen during :term:`fit`. .. versionadded:: 0.24 feature_names_in_ : ndarray of shape (`n_features_in_`,) Names of features seen during :term:`fit`. Defined only when `X` has feature names that are all strings. .. versionadded:: 1.0 feature_importances_ : ndarray of shape (n_features,) Return impurity-based feature importances (the higher, the more important the feature). Warning: impurity-based feature importances can be misleading for high cardinality features (many unique values). See :func:`sklearn.inspection.permutation_importance` as an alternative. n_outputs_ : int The number of outputs when ``fit`` is performed. tree_ : Tree instance The underlying Tree object. Please refer to ``help(sklearn.tree._tree.Tree)`` for attributes of Tree object and :ref:`sphx_glr_auto_examples_tree_plot_unveil_tree_structure.py` for basic usage of these attributes. See Also -------- ExtraTreeClassifier : An extremely randomized tree classifier. sklearn.ensemble.ExtraTreesClassifier : An extra-trees classifier. sklearn.ensemble.ExtraTreesRegressor : An extra-trees regressor. Notes ----- The default values for the parameters controlling the size of the trees (e.g. ``max_depth``, ``min_samples_leaf``, etc.) lead to fully grown and unpruned trees which can potentially be very large on some data sets. To reduce memory consumption, the complexity and size of the trees should be controlled by setting those parameter values. References ---------- .. [1] P. Geurts, D. Ernst., and L. Wehenkel, "Extremely randomized trees", Machine Learning, 63(1), 3-42, 2006. Examples -------- >>> from sklearn.datasets import load_diabetes >>> from sklearn.model_selection import train_test_split >>> from sklearn.ensemble import BaggingRegressor >>> from sklearn.tree import ExtraTreeRegressor >>> X, y = load_diabetes(return_X_y=True) >>> X_train, X_test, y_train, y_test = train_test_split( ... X, y, random_state=0) >>> extra_tree = ExtraTreeRegressor(random_state=0) >>> reg = BaggingRegressor(extra_tree, random_state=0).fit( ... X_train, y_train) >>> reg.score(X_test, y_test) 0.33... """ def __init__( self, *, criterion="squared_error", splitter="random", max_depth=None, min_samples_split=2, min_samples_leaf=1, min_weight_fraction_leaf=0.0, max_features=1.0, random_state=None, min_impurity_decrease=0.0, max_leaf_nodes=None, ccp_alpha=0.0, ): super().__init__( criterion=criterion, splitter=splitter, max_depth=max_depth, min_samples_split=min_samples_split, min_samples_leaf=min_samples_leaf, min_weight_fraction_leaf=min_weight_fraction_leaf, max_features=max_features, max_leaf_nodes=max_leaf_nodes, min_impurity_decrease=min_impurity_decrease, random_state=random_state, ccp_alpha=ccp_alpha, )