""" 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 from __future__ import division import numbers 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 RegressorMixin from ..externals import six from ..feature_selection.from_model import _LearntSelectorMixin from ..utils import check_array from ..utils import check_random_state from ..utils import compute_sample_weight from ..utils.multiclass import check_classification_targets from ..exceptions import NotFittedError from ._criterion import Criterion from ._splitter import Splitter from ._tree import DepthFirstTreeBuilder from ._tree import BestFirstTreeBuilder from ._tree import Tree 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(six.with_metaclass(ABCMeta, BaseEstimator, _LearntSelectorMixin)): """Base class for decision trees. Warning: This class should not be used directly. Use derived classes instead. """ @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_split, class_weight=None, presort=False): 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.random_state = random_state self.max_leaf_nodes = max_leaf_nodes self.min_impurity_split = min_impurity_split self.class_weight = class_weight self.presort = presort self.n_features_ = None self.n_outputs_ = None self.classes_ = None self.n_classes_ = None self.tree_ = None self.max_features_ = None def fit(self, X, y, sample_weight=None, check_input=True, X_idx_sorted=None): """Build a decision tree from the training set (X, y). Parameters ---------- X : array-like or sparse matrix, 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, shape = [n_samples] or [n_samples, n_outputs] The target values (class labels in classification, real numbers in regression). In the regression case, use ``dtype=np.float64`` and ``order='C'`` for maximum efficiency. sample_weight : array-like, shape = [n_samples] or 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. In the case of classification, splits are also ignored if they would result in any single class carrying a negative weight in either child node. check_input : boolean, (default=True) Allow to bypass several input checking. Don't use this parameter unless you know what you do. X_idx_sorted : array-like, shape = [n_samples, n_features], optional 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 : object Returns self. """ random_state = check_random_state(self.random_state) if check_input: X = check_array(X, dtype=DTYPE, accept_sparse="csc") y = check_array(y, ensure_2d=False, dtype=None) 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 = isinstance(self, ClassifierMixin) 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) else: self.classes_ = [None] * self.n_outputs_ self.n_classes_ = [1] * self.n_outputs_ 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 = ((2 ** 31) - 1 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, np.integer)): 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, np.integer)): 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, six.string_types): 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, np.integer)): 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_samples_split <= 1. or 2 <= self.min_samples_split): raise ValueError("min_samples_split must be in at least 2" " or in (0, 1], got %s" % min_samples_split) if not (0. < self.min_samples_leaf <= 0.5 or 1 <= self.min_samples_leaf): raise ValueError("min_samples_leaf must be at least than 1 " "or in (0, 0.5], got %s" % min_samples_leaf) 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, np.integer)): 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 smaller than " "0 or larger than 1").format(max_leaf_nodes)) if sample_weight is not None: if (getattr(sample_weight, "dtype", None) != DOUBLE or not sample_weight.flags.contiguous): sample_weight = np.ascontiguousarray( sample_weight, dtype=DOUBLE) if len(sample_weight.shape) > 1: raise ValueError("Sample weights array has more " "than one dimension: %d" % len(sample_weight.shape)) if len(sample_weight) != n_samples: raise ValueError("Number of weights=%d does not match " "number of samples=%d" % (len(sample_weight), n_samples)) 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 self.min_weight_fraction_leaf != 0. and sample_weight is not None: min_weight_leaf = (self.min_weight_fraction_leaf * np.sum(sample_weight)) else: min_weight_leaf = 0. if self.min_impurity_split < 0.: raise ValueError("min_impurity_split must be greater than or equal " "to 0") presort = self.presort # Allow presort to be 'auto', which means True if the dataset is dense, # otherwise it will be False. if self.presort == 'auto' and issparse(X): presort = False elif self.presort == 'auto': presort = True if presort is True and issparse(X): raise ValueError("Presorting is not supported for sparse " "matrices.") # If multiple trees are built on the same dataset, we only want to # presort once. Splitters now can accept presorted indices if desired, # but do not handle any presorting themselves. Ensemble algorithms # which desire presorting must do presorting themselves and pass that # matrix into each tree. if X_idx_sorted is None and presort: X_idx_sorted = np.asfortranarray(np.argsort(X, axis=0), dtype=np.int32) if presort and X_idx_sorted.shape != X.shape: raise ValueError("The shape of X (X.shape = {}) doesn't match " "the shape of X_idx_sorted (X_idx_sorted" ".shape = {})".format(X.shape, X_idx_sorted.shape)) # 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, self.presort) self.tree_ = Tree(self.n_features_, self.n_classes_, 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_split) else: builder = BestFirstTreeBuilder(splitter, min_samples_split, min_samples_leaf, min_weight_leaf, max_depth, max_leaf_nodes, self.min_impurity_split) builder.build(self.tree_, X, y, sample_weight, X_idx_sorted) if self.n_outputs_ == 1: self.n_classes_ = self.n_classes_[0] self.classes_ = self.classes_[0] return self def _validate_X_predict(self, X, check_input): """Validate X whenever one tries to predict, apply, predict_proba""" if self.tree_ is None: raise NotFittedError("Estimator not fitted, " "call `fit` before exploiting the model.") 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 or 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 : boolean, (default=True) Allow to bypass several input checking. Don't use this parameter unless you know what you do. Returns ------- y : array of shape = [n_samples] or [n_samples, n_outputs] The predicted classes, or the predict values. """ X = self._validate_X_predict(X, check_input) proba = self.tree_.predict(X) n_samples = X.shape[0] # Classification if isinstance(self, ClassifierMixin): if self.n_outputs_ == 1: return self.classes_.take(np.argmax(proba, axis=1), axis=0) else: predictions = np.zeros((n_samples, self.n_outputs_)) 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): """ Returns the index of the leaf that each sample is predicted as. .. versionadded:: 0.17 Parameters ---------- X : array_like or sparse matrix, 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 : boolean, (default=True) Allow to bypass several input checking. Don't use this parameter unless you know what you do. Returns ------- X_leaves : array_like, 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. """ 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 or sparse matrix, 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 : boolean, (default=True) Allow to bypass several input checking. Don't use this parameter unless you know what you do. Returns ------- indicator : sparse csr array, shape = [n_samples, n_nodes] Return a node indicator 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) @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. Returns ------- feature_importances_ : array, shape = [n_features] """ if self.tree_ is None: raise NotFittedError("Estimator not fitted, call `fit` before" " `feature_importances_`.") return self.tree_.compute_feature_importances() # ============================================================================= # Public estimators # ============================================================================= class DecisionTreeClassifier(BaseDecisionTree, ClassifierMixin): """A decision tree classifier. Read more in the :ref:`User Guide `. Parameters ---------- criterion : string, optional (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 : string, optional (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_features : int, float, string or None, optional (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 percentage 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. max_depth : int or None, optional (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, float, optional (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 percentage and `ceil(min_samples_split * n_samples)` are the minimum number of samples for each split. .. versionchanged:: 0.18 Added float values for percentages. min_samples_leaf : int, float, optional (default=1) The minimum number of samples required to be at a leaf node: - If int, then consider `min_samples_leaf` as the minimum number. - If float, then `min_samples_leaf` is a percentage and `ceil(min_samples_leaf * n_samples)` are the minimum number of samples for each node. .. versionchanged:: 0.18 Added float values for percentages. min_weight_fraction_leaf : float, optional (default=0.) The minimum weighted fraction of the input samples required to be at a leaf node. max_leaf_nodes : int or None, optional (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. class_weight : dict, list of dicts, "balanced" or None, optional (default=None) Weights associated with classes in the form ``{class_label: weight}``. If not given, 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. 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. random_state : int, RandomState instance or None, optional (default=None) If int, random_state is the seed used by the random number generator; If RandomState instance, random_state is the random number generator; If None, the random number generator is the RandomState instance used by `np.random`. min_impurity_split : float, optional (default=1e-7) Threshold for early stopping in tree growth. A node will split if its impurity is above the threshold, otherwise it is a leaf. .. versionadded:: 0.18 presort : bool, optional (default=False) Whether to presort the data to speed up the finding of best splits in fitting. For the default settings of a decision tree on large datasets, setting this to true may slow down the training process. When using either a smaller dataset or a restricted depth, this may speed up the training. Attributes ---------- classes_ : array of shape = [n_classes] or a list of such arrays The classes labels (single output problem), or a list of arrays of class labels (multi-output problem). feature_importances_ : array 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]_. max_features_ : int, The inferred value of max_features. n_classes_ : int or list 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 object The underlying Tree object. See also -------- DecisionTreeRegressor 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", http://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. ]) """ 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_split=1e-7, class_weight=None, presort=False): super(DecisionTreeClassifier, self).__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_split=min_impurity_split, presort=presort) 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. check_input : boolean, (default=True) Allow to bypass several input checking. Don't use this parameter unless you know what you do. Parameters ---------- X : array-like or 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 ------- p : array of shape = [n_samples, n_classes], or a 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 `classes_`. """ 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 or 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 ------- p : array of shape = [n_samples, n_classes], or a 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 `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(BaseDecisionTree, RegressorMixin): """A decision tree regressor. Read more in the :ref:`User Guide `. Parameters ---------- criterion : string, optional (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 : string, optional (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_features : int, float, string or None, optional (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 percentage 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. max_depth : int or None, optional (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, float, optional (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 percentage and `ceil(min_samples_split * n_samples)` are the minimum number of samples for each split. .. versionchanged:: 0.18 Added float values for percentages. min_samples_leaf : int, float, optional (default=1) The minimum number of samples required to be at a leaf node: - If int, then consider `min_samples_leaf` as the minimum number. - If float, then `min_samples_leaf` is a percentage and `ceil(min_samples_leaf * n_samples)` are the minimum number of samples for each node. .. versionchanged:: 0.18 Added float values for percentages. min_weight_fraction_leaf : float, optional (default=0.) The minimum weighted fraction of the input samples required to be at a leaf node. max_leaf_nodes : int or None, optional (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. random_state : int, RandomState instance or None, optional (default=None) If int, random_state is the seed used by the random number generator; If RandomState instance, random_state is the random number generator; If None, the random number generator is the RandomState instance used by `np.random`. min_impurity_split : float, optional (default=1e-7) Threshold for early stopping in tree growth. If the impurity of a node is below the threshold, the node is a leaf. .. versionadded:: 0.18 presort : bool, optional (default=False) Whether to presort the data to speed up the finding of best splits in fitting. For the default settings of a decision tree on large datasets, setting this to true may slow down the training process. When using either a smaller dataset or a restricted depth, this may speed up the training. Attributes ---------- feature_importances_ : array 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]_. 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 object The underlying Tree object. See also -------- DecisionTreeClassifier 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", http://www.stat.berkeley.edu/~breiman/RandomForests/cc_home.htm Examples -------- >>> from sklearn.datasets import load_boston >>> from sklearn.model_selection import cross_val_score >>> from sklearn.tree import DecisionTreeRegressor >>> boston = load_boston() >>> regressor = DecisionTreeRegressor(random_state=0) >>> cross_val_score(regressor, boston.data, boston.target, cv=10) ... # doctest: +SKIP ... array([ 0.61..., 0.57..., -0.34..., 0.41..., 0.75..., 0.07..., 0.29..., 0.33..., -1.42..., -1.77...]) """ 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_split=1e-7, presort=False): super(DecisionTreeRegressor, self).__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_split=min_impurity_split, presort=presort) 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 `. See also -------- ExtraTreeRegressor, ExtraTreesClassifier, ExtraTreesRegressor References ---------- .. [1] P. Geurts, D. Ernst., and L. Wehenkel, "Extremely randomized trees", Machine Learning, 63(1), 3-42, 2006. """ 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_split=1e-7, class_weight=None): super(ExtraTreeClassifier, self).__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_split=min_impurity_split, random_state=random_state) 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 `. See also -------- ExtraTreeClassifier, ExtraTreesClassifier, ExtraTreesRegressor References ---------- .. [1] P. Geurts, D. Ernst., and L. Wehenkel, "Extremely randomized trees", Machine Learning, 63(1), 3-42, 2006. """ 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_split=1e-7, max_leaf_nodes=None): super(ExtraTreeRegressor, self).__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_split=min_impurity_split, random_state=random_state)