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)[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. When max_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 : 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.
New in version 0.18: Mean Absolute Error (MAE) criterion.
 splitter : string, 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_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 fraction and ceil(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_leaf : int, 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 and ceil(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_leaf : float, 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_features : int, 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 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 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_decrease : float, 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_split : float, (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_nodes : int 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.
Attributes: feature_importances_
Return the feature importances.
See also
ExtraTreeClassifier
,sklearn.ensemble.ExtraTreesClassifier
,sklearn.ensemble.ExtraTreesRegressor
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. Methods
apply
(X[, check_input])Returns the index of the leaf that each sample is predicted as. 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). get_params
([deep])Get parameters for this estimator. predict
(X[, check_input])Predict class or regression value for X. score
(X, y[, sample_weight])Returns the coefficient of determination R^2 of the prediction. set_params
(**params)Set the parameters of this estimator. 
__init__
(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)[source]¶

apply
(X, check_input=True)[source]¶ Returns the index of the leaf that each sample is predicted as.
New in version 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 sparsecsr_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.

decision_path
(X, check_input=True)[source]¶ Return the decision path in the tree
New in version 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 sparsecsr_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.

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
(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:  X : arraylike 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
. y : arraylike, shape = [n_samples] or [n_samples, n_outputs]
The target values (real numbers). Use
dtype=np.float64
andorder='C'
for maximum efficiency. sample_weight : arraylike, 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_input : boolean, (default=True)
Allow to bypass several input checking. Don’t use this parameter unless you know what you do.
 X_idx_sorted : arraylike, 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

get_params
(deep=True)[source]¶ Get parameters for this estimator.
Parameters:  deep : boolean, optional
If True, will return the parameters for this estimator and contained subobjects that are estimators.
Returns:  params : mapping of string to any
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 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_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.

score
(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:  X : arraylike, 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.
 y : arraylike, shape = (n_samples) or (n_samples, n_outputs)
True values for X.
 sample_weight : arraylike, shape = [n_samples], optional
Sample weights.
Returns:  score : float
R^2 of self.predict(X) wrt. y.

set_params
(**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