3.2.3.3.4. sklearn.ensemble.ExtraTreesRegressor¶
- class sklearn.ensemble.ExtraTreesRegressor(n_estimators=10, criterion='mse', max_depth=None, min_samples_split=2, min_samples_leaf=1, max_features='auto', bootstrap=False, oob_score=False, n_jobs=1, random_state=None, verbose=0, min_density=None, compute_importances=None)¶
An extra-trees regressor.
This class implements a meta estimator that fits a number of randomized decision trees (a.k.a. extra-trees) on various sub-samples of the dataset and use averaging to improve the predictive accuracy and control over-fitting.
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. The only supported criterion is “mse” for the mean squared error. Note: this parameter is tree-specific.
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: this parameter is tree-specific.
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. Note: this parameter is tree-specific.
min_samples_split : integer, optional (default=2)
The minimum number of samples required to split an internal node. Note: this parameter is tree-specific.
min_samples_leaf : integer, optional (default=1)
The minimum number of samples in newly created leaves. A split is discarded if after the split, one of the leaves would contain less then min_samples_leaf samples. Note: this parameter is tree-specific.
bootstrap : boolean, optional (default=False)
Whether bootstrap samples are used when building trees. Note: this parameter is tree-specific.
oob_score : bool
Whether to use out-of-bag samples to estimate the generalization error.
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.
See also
- sklearn.tree.ExtraTreeRegressor
- Base estimator for this ensemble.
- RandomForestRegressor
- Ensemble regressor using trees with optimal splits.
References
[R110] P. Geurts, D. Ernst., and L. Wehenkel, “Extremely randomized trees”, Machine Learning, 63(1), 3-42, 2006. Attributes
estimators_: list of DecisionTreeRegressor The collection of fitted sub-estimators. feature_importances_ array of shape = [n_features] The feature importances (the higher, the more important the feature). oob_score_ float Score of the training dataset obtained using an out-of-bag estimate. oob_prediction_ array of shape = [n_samples] Prediction computed with out-of-bag estimate on the training set. Methods
apply(X) Apply trees in the forest to X, return leaf indices. fit(X, y[, sample_weight]) Build a forest of trees from the training set (X, y). fit_transform(X[, y]) Fit to data, then transform it. get_params([deep]) Get parameters for this estimator. predict(X) Predict regression target for X. score(X, y) Returns the coefficient of determination R^2 of the prediction. set_params(**params) Set the parameters of this estimator. transform(X[, threshold]) Reduce X to its most important features. - __init__(n_estimators=10, criterion='mse', max_depth=None, min_samples_split=2, min_samples_leaf=1, max_features='auto', bootstrap=False, oob_score=False, n_jobs=1, random_state=None, verbose=0, min_density=None, compute_importances=None)¶
- apply(X)¶
Apply trees in the forest to X, return leaf indices.
Parameters : X : array-like, shape = [n_samples, n_features]
Input data.
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.
- 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)¶
Build a forest of trees from the training set (X, y).
Parameters : X : array-like of shape = [n_samples, n_features]
The training input samples.
y : array-like, shape = [n_samples] or [n_samples, n_outputs]
The target values (integers that correspond to classes in classification, real numbers in regression).
sample_weight : array-like, 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.
- fit_transform(X, y=None, **fit_params)¶
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)¶
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)¶
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 : array-like of shape = [n_samples, n_features]
The input samples.
Returns : y: array of shape = [n_samples] or [n_samples, n_outputs] :
The predicted values.
- score(X, y)¶
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 : array-like, shape = [n_samples, n_features]
Training set.
y : array-like, shape = [n_samples]
Returns : z : float
- set_params(**params)¶
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)¶
Reduce X to its most important features.
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.