sklearn.tree.ExtraTreeRegressor

A sklearn.tree.ExtraTreeRegressor is a Regresssion Extra-Trees Learning System within sklearn.tree module.

• AKA: ExtraTreeRegressor, tree.ExtraTreeRegressor.
• Context
  • Usage:

1) Import Regression Extra-Trees Learning System from scikit-learn : from sklearn.tree import ExtraTreeRegressor

2) Create design matrix X and response vector Y

3) Create Extra-Trees Regressor object: ETreg=ExtraTreeRegressor(criterion=’gini’, splitter=’best’[, max_depth=None, min_samples_split=2, min_samples_leaf=1,...])

4) Choose method(s):

• ETreg.apply(X[, check_input]), returns the leaf index for each sample predictor.
• ETreg.decision_path(X[, check_input]), returns the decision path in the tree.
• ETreg.fit(X, y[, sample_weight, check_input,...]) builds a decision tree regressor from the training set (X, y).
• ETreg.get_params([deep]) returns parameters for this estimator.
• ETreg.predict(X[, check_input]), predicts regression value for X.
• ETreg.score(X, y[, sample_weight]), returns the coefficient of determination R^2 of the prediction.
• ETreg.set_params(**params), sets the parameters of this estimator.

• Example(s):
  • …
• Counter-Example(s):
  • sklearn.ensemble.ExtraTreesClassifier
  • sklearn.ensemble.ExtraTreesRegressor
  • sklearn.tree.DecisionTreeRegressor
  • sklearn.tree.DecisionTreeClassifier
  • sklearn.tree.ExtraTreeClassifier
• See: Decision Tree, Classification System, Regularization Task, Ridge Regression Task, Kernel-based Classification Algorithm.

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References

2017

• (Scikit-Learn, 2017) ⇒ http://scikit-learn.org/stable/modules/generated/sklearn.tree.ExtraTreeRegressor.html Retrieved:2017-10-22
  • QUOTE: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)

    An extremely randomized tree regressor.

     Extra-trees 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: Extra-trees should only be used within ensemble methods.

    Read more in the User Guide.

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