""" 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 from abc import ABCMeta from abc import abstractmethod from math import ceil 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_array 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.validation import _deprecate_positional_args 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, "entropy": _criterion.Entropy} CRITERIA_REG = {"mse": _criterion.MSE, "friedman_mse": _criterion.FriedmanMSE, "mae": _criterion.MAE} 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. """ @abstractmethod @_deprecate_positional_args 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, min_impurity_split, class_weight=None, presort='deprecated', 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.min_impurity_split = min_impurity_split self.class_weight = class_weight self.presort = presort 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, X_idx_sorted=None): random_state = check_random_state(self.random_state) if self.ccp_alpha < 0.0: raise ValueError("ccp_alpha must be greater than or equal to 0") if check_input: # Need to validate separately here. # We can't pass multi_ouput=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") # Determine output settings n_samples, self.n_features_ = 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=np.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) # Check parameters max_depth = (np.iinfo(np.int32).max if self.max_depth is None else self.max_depth) max_leaf_nodes = (-1 if self.max_leaf_nodes is None else self.max_leaf_nodes) if isinstance(self.min_samples_leaf, numbers.Integral): if not 1 <= self.min_samples_leaf: raise ValueError("min_samples_leaf must be at least 1 " "or in (0, 0.5], got %s" % self.min_samples_leaf) min_samples_leaf = self.min_samples_leaf else: # float if not 0. < self.min_samples_leaf <= 0.5: raise ValueError("min_samples_leaf must be at least 1 " "or in (0, 0.5], got %s" % self.min_samples_leaf) min_samples_leaf = int(ceil(self.min_samples_leaf * n_samples)) if isinstance(self.min_samples_split, numbers.Integral): if not 2 <= self.min_samples_split: raise ValueError("min_samples_split must be an integer " "greater than 1 or a float in (0.0, 1.0]; " "got the integer %s" % self.min_samples_split) min_samples_split = self.min_samples_split else: # float if not 0. < self.min_samples_split <= 1.: raise ValueError("min_samples_split must be an integer " "greater than 1 or a float in (0.0, 1.0]; " "got the float %s" % self.min_samples_split) 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_))) else: max_features = self.n_features_ elif self.max_features == "sqrt": max_features = max(1, int(np.sqrt(self.n_features_))) elif self.max_features == "log2": max_features = max(1, int(np.log2(self.n_features_))) else: raise ValueError("Invalid value for max_features. " "Allowed string values are 'auto', " "'sqrt' or 'log2'.") elif self.max_features is None: max_features = self.n_features_ 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_)) else: max_features = 0 self.max_features_ = max_features if len(y) != n_samples: raise ValueError("Number of labels=%d does not match " "number of samples=%d" % (len(y), n_samples)) if not 0 <= self.min_weight_fraction_leaf <= 0.5: raise ValueError("min_weight_fraction_leaf must in [0, 0.5]") if max_depth <= 0: raise ValueError("max_depth must be greater than zero. ") if not (0 < max_features <= self.n_features_): raise ValueError("max_features must be in (0, n_features]") if not isinstance(max_leaf_nodes, numbers.Integral): raise ValueError("max_leaf_nodes must be integral number but was " "%r" % max_leaf_nodes) if -1 < max_leaf_nodes < 2: raise ValueError(("max_leaf_nodes {0} must be either None " "or larger than 1").format(max_leaf_nodes)) 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)) min_impurity_split = self.min_impurity_split if min_impurity_split is not None: warnings.warn("The min_impurity_split parameter is deprecated. " "Its default value has changed from 1e-7 to 0 in " "version 0.23, and it will be removed in 0.25. " "Use the min_impurity_decrease parameter instead.", FutureWarning) if min_impurity_split < 0.: raise ValueError("min_impurity_split must be greater than " "or equal to 0") else: min_impurity_split = 0 if self.min_impurity_decrease < 0.: raise ValueError("min_impurity_decrease must be greater than " "or equal to 0") if self.presort != 'deprecated': warnings.warn("The parameter 'presort' is deprecated and has no " "effect. It will be removed in v0.24. You can " "suppress this warning by not passing any value " "to the 'presort' parameter.", FutureWarning) # 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) 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_, self.n_classes_, self.n_outputs_) else: self.tree_ = Tree(self.n_features_, # TODO: tree should'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, min_impurity_split) else: builder = BestFirstTreeBuilder(splitter, min_samples_split, min_samples_leaf, min_weight_leaf, max_depth, max_leaf_nodes, self.min_impurity_decrease, min_impurity_split) builder.build(self.tree_, X, y, sample_weight, X_idx_sorted) 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 X whenever one tries to predict, apply, predict_proba""" if check_input: X = check_array(X, dtype=DTYPE, accept_sparse="csr") 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") n_features = X.shape[1] if self.n_features_ != n_features: raise ValueError("Number of features of the model must " "match the input. Model n_features is %s and " "input n_features is %s " % (self.n_features_, n_features)) 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 do. 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 do. 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 do. 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: raise ValueError("ccp_alpha must be greater than or equal to 0") 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_, n_classes, self.n_outputs_) else: pruned_tree = Tree(self.n_features_, # 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"}, default="gini" The function to measure the quality of a split. Supported criteria are "gini" for the Gini impurity and "entropy" for the information gain. 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 `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`. 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, 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 min_impurity_split : float, default=0 Threshold for early stopping in tree growth. A node will split if its impurity is above the threshold, otherwise it is a leaf. .. deprecated:: 0.19 ``min_impurity_split`` has been deprecated in favor of ``min_impurity_decrease`` in 0.19. The default value of ``min_impurity_split`` has changed from 1e-7 to 0 in 0.23 and it will be removed in 0.25. Use ``min_impurity_decrease`` instead. 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. presort : deprecated, default='deprecated' This parameter is deprecated and will be removed in v0.24. .. deprecated:: 0.22 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_ : int The number of features when ``fit`` is performed. n_outputs_ : int The number of outputs when ``fit`` is performed. tree_ : Tree 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. 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. ]) """ @_deprecate_positional_args def __init__(self, *, criterion="gini", splitter="best", max_depth=None, min_samples_split=2, min_samples_leaf=1, min_weight_fraction_leaf=0., max_features=None, random_state=None, max_leaf_nodes=None, min_impurity_decrease=0., min_impurity_split=None, class_weight=None, presort='deprecated', 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, min_impurity_split=min_impurity_split, presort=presort, ccp_alpha=ccp_alpha) def fit(self, X, y, sample_weight=None, check_input=True, X_idx_sorted=None): """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 do. X_idx_sorted : array-like of shape (n_samples, n_features), \ default=None The indexes of the sorted training input samples. If many tree are grown on the same dataset, this allows the ordering to be cached between trees. If None, the data will be sorted here. Don't use this parameter unless you know what to do. Returns ------- self : DecisionTreeClassifier Fitted estimator. """ super().fit( X, y, sample_weight=sample_weight, check_input=check_input, X_idx_sorted=X_idx_sorted) 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 do. 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 class DecisionTreeRegressor(RegressorMixin, BaseDecisionTree): """A decision tree regressor. Read more in the :ref:`User Guide `. Parameters ---------- criterion : {"mse", "friedman_mse", "mae"}, default="mse" The function to measure the quality of a split. Supported criteria are "mse" 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, and "mae" for the mean absolute error, which minimizes the L1 loss using the median of each terminal node. .. versionadded:: 0.18 Mean Absolute Error (MAE) 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 `int(max_features * n_features)` 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`. 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, 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 min_impurity_split : float, (default=0) Threshold for early stopping in tree growth. A node will split if its impurity is above the threshold, otherwise it is a leaf. .. deprecated:: 0.19 ``min_impurity_split`` has been deprecated in favor of ``min_impurity_decrease`` in 0.19. The default value of ``min_impurity_split`` has changed from 1e-7 to 0 in 0.23 and it will be removed in 0.25. Use ``min_impurity_decrease`` instead. presort : deprecated, default='deprecated' This parameter is deprecated and will be removed in v0.24. .. deprecated:: 0.22 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_ : int The number of features when ``fit`` is performed. n_outputs_ : int The number of outputs when ``fit`` is performed. tree_ : Tree 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...]) """ @_deprecate_positional_args def __init__(self, *, criterion="mse", splitter="best", max_depth=None, min_samples_split=2, min_samples_leaf=1, min_weight_fraction_leaf=0., max_features=None, random_state=None, max_leaf_nodes=None, min_impurity_decrease=0., min_impurity_split=None, presort='deprecated', 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, min_impurity_split=min_impurity_split, presort=presort, ccp_alpha=ccp_alpha) def fit(self, X, y, sample_weight=None, check_input=True, X_idx_sorted=None): """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 do. X_idx_sorted : array-like of shape (n_samples, n_features), \ default=None The indexes of the sorted training input samples. If many tree are grown on the same dataset, this allows the ordering to be cached between trees. If None, the data will be sorted here. Don't use this parameter unless you know what to do. Returns ------- self : DecisionTreeRegressor Fitted estimator. """ super().fit( X, y, sample_weight=sample_weight, check_input=check_input, X_idx_sorted=X_idx_sorted) return self @property def classes_(self): # TODO: Remove method in 0.24 msg = ("the classes_ attribute is to be deprecated from version " "0.22 and will be removed in 0.24.") warnings.warn(msg, FutureWarning) return np.array([None] * self.n_outputs_) @property def n_classes_(self): # TODO: Remove method in 0.24 msg = ("the n_classes_ attribute is to be deprecated from version " "0.22 and will be removed in 0.24.") warnings.warn(msg, FutureWarning) return np.array([1] * self.n_outputs_, dtype=np.intp) 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"}, default="gini" The function to measure the quality of a split. Supported criteria are "gini" for the Gini impurity and "entropy" for the information gain. 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="auto" 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`. 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, 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 min_impurity_split : float, (default=0) Threshold for early stopping in tree growth. A node will split if its impurity is above the threshold, otherwise it is a leaf. .. deprecated:: 0.19 ``min_impurity_split`` has been deprecated in favor of ``min_impurity_decrease`` in 0.19. The default value of ``min_impurity_split`` has changed from 1e-7 to 0 in 0.23 and it will be removed in 0.25. Use ``min_impurity_decrease`` instead. 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_ : int The number of features when ``fit`` is performed. n_outputs_ : int The number of outputs when ``fit`` is performed. tree_ : Tree 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. 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... """ @_deprecate_positional_args def __init__(self, *, criterion="gini", splitter="random", max_depth=None, min_samples_split=2, min_samples_leaf=1, min_weight_fraction_leaf=0., max_features="auto", random_state=None, max_leaf_nodes=None, min_impurity_decrease=0., min_impurity_split=None, 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, min_impurity_split=min_impurity_split, 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 : {"mse", "friedman_mse", "mae"}, default="mse" The function to measure the quality of a split. Supported criteria are "mse" for the mean squared error, which is equal to variance reduction as feature selection criterion, and "mae" for the mean absolute error. .. versionadded:: 0.18 Mean Absolute Error (MAE) 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="auto" 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=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`. 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, 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 min_impurity_split : float, (default=0) Threshold for early stopping in tree growth. A node will split if its impurity is above the threshold, otherwise it is a leaf. .. deprecated:: 0.19 ``min_impurity_split`` has been deprecated in favor of ``min_impurity_decrease`` in 0.19. The default value of ``min_impurity_split`` has changed from 1e-7 to 0 in 0.23 and it will be removed in 0.25. Use ``min_impurity_decrease`` instead. 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_ : int The number of features when ``fit`` is performed. 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 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... """ @_deprecate_positional_args def __init__(self, *, criterion="mse", splitter="random", max_depth=None, min_samples_split=2, min_samples_leaf=1, min_weight_fraction_leaf=0., max_features="auto", random_state=None, min_impurity_decrease=0., min_impurity_split=None, 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, min_impurity_split=min_impurity_split, random_state=random_state, ccp_alpha=ccp_alpha)