sklearn.tree
.ExtraTreeRegressor¶

class
sklearn.tree.
ExtraTreeRegressor
(*, criterion='mse', splitter='random', max_depth=None, min_samples_split=2, min_samples_leaf=1, min_weight_fraction_leaf=0.0, max_features='auto', random_state=None, min_impurity_decrease=0.0, min_impurity_split=None, max_leaf_nodes=None, ccp_alpha=0.0)[source]¶ An extremely randomized tree regressor.
Extratrees 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. Whenmax_features
is set 1, this amounts to building a totally random decision tree.Warning: Extratrees should only be used within ensemble methods.
Read more in the 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.
New in version 0.18: Mean Absolute Error (MAE) criterion.
New in version 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_depthint, 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_splitint 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 andceil(min_samples_split * n_samples)
are the minimum number of samples for each split.
Changed in version 0.18: Added float values for fractions.
 min_samples_leafint 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 andceil(min_samples_leaf * n_samples)
are the minimum number of samples for each node.
Changed in version 0.18: Added float values for fractions.
 min_weight_fraction_leaffloat, 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_featuresint, 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 andint(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_stateint, RandomState instance or None, default=None
Used to pick randomly the
max_features
used at each split. See Glossary for details. min_impurity_decreasefloat, 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, andN_t_R
is the number of samples in the right child.N
,N_t
,N_t_R
andN_t_L
all refer to the weighted sum, ifsample_weight
is passed.New in version 0.19.
 min_impurity_splitfloat, default=None
Threshold for early stopping in tree growth. A node will split if its impurity is above the threshold, otherwise it is a leaf.
Deprecated since version 0.19:
min_impurity_split
has been deprecated in favor ofmin_impurity_decrease
in 0.19. The default value ofmin_impurity_split
has changed from 1e7 to 0 in 0.23 and it will be removed in 1.0 (renaming of 0.25). Usemin_impurity_decrease
instead. max_leaf_nodesint, default=None
Grow a tree with
max_leaf_nodes
in bestfirst fashion. Best nodes are defined as relative reduction in impurity. If None then unlimited number of leaf nodes. ccp_alphanonnegative float, default=0.0
Complexity parameter used for Minimal CostComplexity Pruning. The subtree with the largest cost complexity that is smaller than
ccp_alpha
will be chosen. By default, no pruning is performed. See Minimal CostComplexity Pruning for details.New in version 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 the feature importances.
 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 Understanding the decision tree structure for basic usage of these attributes.
See also
ExtraTreeClassifier
An extremely randomized tree classifier.
sklearn.ensemble.ExtraTreesClassifier
An extratrees classifier.
sklearn.ensemble.ExtraTreesRegressor
An extratrees 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), 342, 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...
Methods
apply
(X[, check_input])Return the index of the leaf that each sample is predicted as.
cost_complexity_pruning_path
(X, y[, …])Compute the pruning path during Minimal CostComplexity Pruning.
decision_path
(X[, check_input])Return the decision path in the tree.
fit
(X, y[, sample_weight, check_input, …])Build a decision tree regressor from the training set (X, y).
Return the depth of the decision tree.
Return the number of leaves of the decision tree.
get_params
([deep])Get parameters for this estimator.
predict
(X[, check_input])Predict class or regression value for X.
score
(X, y[, sample_weight])Return the coefficient of determination \(R^2\) of the prediction.
set_params
(**params)Set the parameters of this estimator.

apply
(X, check_input=True)[source]¶ Return the index of the leaf that each sample is predicted as.
New in version 0.17.
 Parameters
 X{arraylike, 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 sparsecsr_matrix
. check_inputbool, default=True
Allow to bypass several input checking. Don’t use this parameter unless you know what you do.
 Returns
 X_leavesarraylike 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.

cost_complexity_pruning_path
(X, y, sample_weight=None)[source]¶ Compute the pruning path during Minimal CostComplexity Pruning.
See Minimal CostComplexity Pruning for details on the pruning process.
 Parameters
 X{arraylike, 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 sparsecsc_matrix
. yarraylike of shape (n_samples,) or (n_samples, n_outputs)
The target values (class labels) as integers or strings.
 sample_weightarraylike 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
Bunch
Dictionarylike object, with the following attributes.
 ccp_alphasndarray
Effective alphas of subtree during pruning.
 impuritiesndarray
Sum of the impurities of the subtree leaves for the corresponding alpha value in
ccp_alphas
.
 ccp_path

decision_path
(X, check_input=True)[source]¶ Return the decision path in the tree.
New in version 0.18.
 Parameters
 X{arraylike, 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 sparsecsr_matrix
. check_inputbool, default=True
Allow to bypass several input checking. Don’t use this parameter unless you know what you do.
 Returns
 indicatorsparse 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.

property
feature_importances_
¶ 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: impuritybased feature importances can be misleading for high cardinality features (many unique values). See
sklearn.inspection.permutation_importance
as an alternative. Returns
 feature_importances_ndarray of shape (n_features,)
Normalized total reduction of criteria by feature (Gini importance).

fit
(X, y, sample_weight=None, check_input=True, X_idx_sorted='deprecated')[source]¶ Build a decision tree regressor from the training set (X, y).
 Parameters
 X{arraylike, 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 sparsecsc_matrix
. yarraylike of shape (n_samples,) or (n_samples, n_outputs)
The target values (real numbers). Use
dtype=np.float64
andorder='C'
for maximum efficiency. sample_weightarraylike 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_inputbool, default=True
Allow to bypass several input checking. Don’t use this parameter unless you know what you do.
 X_idx_sorteddeprecated, default=”deprecated”
This parameter is deprecated and has no effect. It will be removed in 1.1 (renaming of 0.26).
Deprecated since version 0.24.
 Returns
 selfDecisionTreeRegressor
Fitted estimator.

get_depth
()[source]¶ 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_depthint
The maximum depth of the tree.

get_n_leaves
()[source]¶ Return the number of leaves of the decision tree.
 Returns
 self.tree_.n_leavesint
Number of leaves.

get_params
(deep=True)[source]¶ Get parameters for this estimator.
 Parameters
 deepbool, default=True
If True, will return the parameters for this estimator and contained subobjects that are estimators.
 Returns
 paramsdict
Parameter names mapped to their values.

predict
(X, check_input=True)[source]¶ 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{arraylike, 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 sparsecsr_matrix
. check_inputbool, default=True
Allow to bypass several input checking. Don’t use this parameter unless you know what you do.
 Returns
 yarraylike of shape (n_samples,) or (n_samples, n_outputs)
The predicted classes, or the predict values.

score
(X, y, sample_weight=None)[source]¶ Return the coefficient of determination \(R^2\) of the prediction.
The coefficient \(R^2\) is defined as \((1  \frac{u}{v})\), where \(u\) is the residual sum of squares
((y_true  y_pred) ** 2).sum()
and \(v\) is the total sum of squares((y_true  y_true.mean()) ** 2).sum()
. The best possible score is 1.0 and it can be negative (because the model can be arbitrarily worse). A constant model that always predicts the expected value ofy
, disregarding the input features, would get a \(R^2\) score of 0.0. Parameters
 Xarraylike of shape (n_samples, n_features)
Test samples. For some estimators this may be a precomputed kernel matrix or a list of generic objects instead with shape
(n_samples, n_samples_fitted)
, wheren_samples_fitted
is the number of samples used in the fitting for the estimator. yarraylike of shape (n_samples,) or (n_samples, n_outputs)
True values for
X
. sample_weightarraylike of shape (n_samples,), default=None
Sample weights.
 Returns
 scorefloat
\(R^2\) of
self.predict(X)
wrt.y
.
Notes
The \(R^2\) score used when calling
score
on a regressor usesmultioutput='uniform_average'
from version 0.23 to keep consistent with default value ofr2_score
. This influences thescore
method of all the multioutput regressors (except forMultiOutputRegressor
).

set_params
(**params)[source]¶ Set the parameters of this estimator.
The method works on simple estimators as well as on nested objects (such as
Pipeline
). The latter have parameters of the form<component>__<parameter>
so that it’s possible to update each component of a nested object. Parameters
 **paramsdict
Estimator parameters.
 Returns
 selfestimator instance
Estimator instance.