3.2.4.3.4. sklearn.ensemble
.ExtraTreesRegressor¶

class
sklearn.ensemble.
ExtraTreesRegressor
(n_estimators=10, criterion=’mse’, max_depth=None, min_samples_split=2, min_samples_leaf=1, min_weight_fraction_leaf=0.0, max_features=’auto’, max_leaf_nodes=None, min_impurity_decrease=0.0, min_impurity_split=None, bootstrap=False, oob_score=False, n_jobs=1, random_state=None, verbose=0, warm_start=False)[source]¶ An extratrees regressor.
This class implements a meta estimator that fits a number of randomized decision trees (a.k.a. extratrees) on various subsamples of the dataset and use averaging to improve the predictive accuracy and control overfitting.
Read more in the User Guide.
Parameters: n_estimators : integer, optional (default=10)
The number of trees in the forest.
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.
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 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 : integer 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.
Changed in version 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.
Changed in version 0.18: Added float values for percentages.
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_leaf_nodes : int or None, optional (default=None)
Grow trees with
max_leaf_nodes
in bestfirst fashion. Best nodes are defined as relative reduction in impurity. If None then unlimited number of leaf nodes.min_impurity_split : float,
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 and will be removed in 0.21. Usemin_impurity_decrease
instead.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.
bootstrap : boolean, optional (default=False)
Whether bootstrap samples are used when building trees.
oob_score : bool, optional (default=False)
Whether to use outofbag samples to estimate the R^2 on unseen data.
n_jobs : integer, optional (default=1)
The number of jobs to run in parallel for both fit and predict. If 1, then the number of jobs is set to the number of cores.
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.
verbose : int, optional (default=0)
Controls the verbosity of the tree building process.
warm_start : bool, optional (default=False)
When set to
True
, reuse the solution of the previous call to fit and add more estimators to the ensemble, otherwise, just fit a whole new forest.Attributes
feature_importances_
Return the feature importances (the higher, the more important the feature). estimators_ (list of DecisionTreeRegressor) The collection of fitted subestimators. n_features_ (int) The number of features. n_outputs_ (int) The number of outputs. oob_score_ (float) Score of the training dataset obtained using an outofbag estimate. oob_prediction_ (array of shape = [n_samples]) Prediction computed with outofbag estimate on the training set. See also
sklearn.tree.ExtraTreeRegressor
 Base estimator for this ensemble.
RandomForestRegressor
 Ensemble regressor using trees with optimal splits.
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
[R395395] P. Geurts, D. Ernst., and L. Wehenkel, “Extremely randomized trees”, Machine Learning, 63(1), 342, 2006. Methods
apply
(X)Apply trees in the forest to X, return leaf indices. decision_path
(X)Return the decision path in the forest fit
(X, y[, sample_weight])Build a forest of trees from the training set (X, y). get_params
([deep])Get parameters for this estimator. predict
(X)Predict regression target 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__
(n_estimators=10, criterion=’mse’, max_depth=None, min_samples_split=2, min_samples_leaf=1, min_weight_fraction_leaf=0.0, max_features=’auto’, max_leaf_nodes=None, min_impurity_decrease=0.0, min_impurity_split=None, bootstrap=False, oob_score=False, n_jobs=1, random_state=None, verbose=0, warm_start=False)[source]¶

apply
(X)[source]¶ Apply trees in the forest to X, return leaf indices.
Parameters: X : arraylike or sparse matrix, shape = [n_samples, n_features]
The input samples. Internally, its dtype will be converted to
dtype=np.float32
. If a sparse matrix is provided, it will be converted into a sparsecsr_matrix
.Returns: X_leaves : array_like, shape = [n_samples, n_estimators]
For each datapoint x in X and for each tree in the forest, return the index of the leaf x ends up in.

decision_path
(X)[source]¶ Return the decision path in the forest
New in version 0.18.
Parameters: X : arraylike or sparse matrix, shape = [n_samples, n_features]
The input samples. Internally, its dtype will be converted to
dtype=np.float32
. If a sparse matrix is provided, it will be converted into a sparsecsr_matrix
.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.
n_nodes_ptr : array of size (n_estimators + 1, )
The columns from indicator[n_nodes_ptr[i]:n_nodes_ptr[i+1]] gives the indicator value for the ith estimator.

feature_importances_
¶  Return the feature importances (the higher, the more important the
 feature).
Returns: feature_importances_ : array, shape = [n_features]

fit
(X, y, sample_weight=None)[source]¶ Build a forest of trees from the training set (X, y).
Parameters: X : arraylike or sparse matrix of shape = [n_samples, n_features]
The training input samples. Internally, its dtype will be converted to
dtype=np.float32
. If a sparse matrix is provided, it will be converted into a sparsecsc_matrix
.y : arraylike, shape = [n_samples] or [n_samples, n_outputs]
The target values (class labels in classification, real numbers in regression).
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. 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.
Returns: self : object
Returns self.

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)[source]¶ Predict regression target for X.
The predicted regression target of an input sample is computed as the mean predicted regression targets of the trees in the forest.
Parameters: X : arraylike or sparse matrix of shape = [n_samples, n_features]
The input samples. Internally, its dtype will be converted to
dtype=np.float32
. If a sparse matrix is provided, it will be converted into a sparsecsr_matrix
.Returns: y : array of shape = [n_samples] or [n_samples, n_outputs]
The predicted 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.
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.