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
 criterionstring, 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.
New in version 0.18: Mean Absolute Error (MAE) criterion.
 splitterstring, optional (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 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_splitint, 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 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, float, optional (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, optional (default=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, string or None, optional (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, 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_decreasefloat, optional (default=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=1e7)
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
will change from 1e7 to 0 in 0.23 and it will be removed in 0.25. Usemin_impurity_decrease
instead. max_leaf_nodesint or None, optional (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, optional (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. n_outputs_int
The number of outputs when
fit
is performed. tree_Tree object
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
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
 R4939d63d5a491
P. Geurts, D. Ernst., and L. Wehenkel, “Extremely randomized trees”, Machine Learning, 63(1), 342, 2006.
Methods
apply
(self, X[, check_input])Returns the index of the leaf that each sample is predicted as.
cost_complexity_pruning_path
(self, X, y[, …])Compute the pruning path during Minimal CostComplexity Pruning.
decision_path
(self, X[, check_input])Return the decision path in the tree
fit
(self, X, y[, sample_weight, …])Build a decision tree regressor from the training set (X, y).
get_depth
(self)Returns the depth of the decision tree.
get_n_leaves
(self)Returns the number of leaves of the decision tree.
get_params
(self[, deep])Get parameters for this estimator.
predict
(self, X[, check_input])Predict class or regression value for X.
score
(self, X, y[, sample_weight])Returns the coefficient of determination R^2 of the prediction.
set_params
(self, \*\*params)Set the parameters of this estimator.

__init__
(self, 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]¶ Initialize self. See help(type(self)) for accurate signature.

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

cost_complexity_pruning_path
(self, X, y, sample_weight=None)[source]¶ Compute the pruning path during Minimal CostComplexity Pruning.
See
ref
:minimal_cost_complexity_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_pathBunch
Dictionarylike object, with 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
.

decision_path
(self, X, check_input=True)[source]¶ Return the decision path in the tree
New in version 0.18.
 Parameters
 Xarray_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 sparsecsr_matrix
. check_inputboolean, (default=True)
Allow to bypass several input checking. Don’t use this parameter unless you know what you do.
 Returns
 indicatorsparse csr array, shape = [n_samples, n_nodes]
Return a node indicator 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.
 Returns
 feature_importances_array, shape = [n_features]

fit
(self, X, y, sample_weight=None, check_input=True, X_idx_sorted=None)[source]¶ Build a decision tree regressor from the training set (X, y).
 Parameters
 Xarraylike 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 sparsecsc_matrix
. yarraylike, shape = [n_samples] or [n_samples, n_outputs]
The target values (real numbers). Use
dtype=np.float64
andorder='C'
for maximum efficiency. sample_weightarraylike, 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.
 check_inputboolean, (default=True)
Allow to bypass several input checking. Don’t use this parameter unless you know what you do.
 X_idx_sortedarraylike, 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
 selfobject

get_depth
(self)[source]¶ Returns the depth of the decision tree.
The depth of a tree is the maximum distance between the root and any leaf.

get_params
(self, deep=True)[source]¶ Get parameters for this estimator.
 Parameters
 deepboolean, optional
If True, will return the parameters for this estimator and contained subobjects that are estimators.
 Returns
 paramsmapping of string to any
Parameter names mapped to their values.

predict
(self, 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
 Xarraylike 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 sparsecsr_matrix
. check_inputboolean, (default=True)
Allow to bypass several input checking. Don’t use this parameter unless you know what you do.
 Returns
 yarray of shape = [n_samples] or [n_samples, n_outputs]
The predicted classes, or the predict values.

score
(self, X, y, sample_weight=None)[source]¶ Returns the coefficient of determination R^2 of the prediction.
The coefficient R^2 is defined as (1  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 of y, disregarding the input features, would get a R^2 score of 0.0.
 Parameters
 Xarraylike, shape = (n_samples, n_features)
Test samples. For some estimators this may be a precomputed kernel matrix instead, shape = (n_samples, n_samples_fitted], where n_samples_fitted is the number of samples used in the fitting for the estimator.
 yarraylike, shape = (n_samples) or (n_samples, n_outputs)
True values for X.
 sample_weightarraylike, shape = [n_samples], optional
Sample weights.
 Returns
 scorefloat
R^2 of self.predict(X) wrt. y.
Notes
The R2 score used when calling
score
on a regressor will usemultioutput='uniform_average'
from version 0.23 to keep consistent withr2_score
. This will influence thescore
method of all the multioutput regressors (except forMultiOutputRegressor
). To specify the default value manually and avoid the warning, please either callr2_score
directly or make a custom scorer withmake_scorer
(the builtin scorer'r2'
usesmultioutput='uniform_average'
).

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