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, 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.
See also
ExtraTreeClassifier
,ExtraTreesClassifier
,ExtraTreesRegressor
References
[R110] P. Geurts, D. Ernst., and L. Wehenkel, “Extremely randomized trees”, Machine Learning, 63(1), 342, 2006. Methods

__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, max_leaf_nodes=None)[source]¶

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)[source]¶ Build a decision tree 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 (class labels in classification, real numbers in regression). In the regression case, 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. 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.
Returns: self : object
Returns self.

fit_transform
(X, y=None, **fit_params)[source]¶ Fit to data, then transform it.
Fits transformer to X and y with optional parameters fit_params and returns a transformed version of X.
Parameters: X : numpy array of shape [n_samples, n_features]
Training set.
y : numpy array of shape [n_samples]
Target values.
Returns: X_new : numpy array of shape [n_samples, n_features_new]
Transformed array.

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 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
.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 regression sum of squares ((y_true  y_pred) ** 2).sum() and v is the residual sum of squares ((y_true  y_true.mean()) ** 2).sum(). Best possible score is 1.0, lower values are worse.
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.

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 former have parameters of the form
<component>__<parameter>
so that it’s possible to update each component of a nested object.Returns: self :

transform
(X, threshold=None)[source]¶ Reduce X to its most important features.
Uses
coef_
orfeature_importances_
to determine the most important features. For models with acoef_
for each class, the absolute sum over the classes is used.Parameters: X : array or scipy sparse matrix of shape [n_samples, n_features]
The input samples.
threshold : string, float or None, optional (default=None)
The threshold value to use for feature selection. Features whose importance is greater or equal are kept while the others are discarded. If “median” (resp. “mean”), then the threshold value is the median (resp. the mean) of the feature importances. A scaling factor (e.g., “1.25*mean”) may also be used. If None and if available, the object attribute
threshold
is used. Otherwise, “mean” is used by default.Returns: X_r : array of shape [n_samples, n_selected_features]
The input samples with only the selected features.
