sklearn.tree.DecisionTreeRegressor

A sklearn.tree.DecisionTreeRegressor is a regression tree learning system within sklearn.tree.

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

1) Import Regression Tree Learning System from scikit-learn : from sklearn.tree import DecisionTreeRegressor

2) Create design matrix X and response vector Y

3) Create Decision Tree Regressor object: DTreg=DecisionTreeRegressor(criterion=’mse’, splitter=’best’[, max_depth=None, min_samples_split=2, min_samples_leaf=1,...])

4) Choose method(s):

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

• Example(s):
  • Decision Tree Regression
  • Multi-output Decision Tree Regression.
• Counter-Example(s):
  • sklearn.tree.DecisionTreeClassifier
  • sklearn.tree.ExtraTreeRegressor
  • sklearn.tree.ExtraTreeClassifier
  • rpart System.
• See: Decision Tree, Regression 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.DecisionTreeRegressor.html Retrieved:2017-10-22
  • QUOTE: class sklearn.tree.DecisionTreeRegressor(criterion=’mse’, splitter=’best’, max_depth=None, min_samples_split=2, min_samples_leaf=1, min_weight_fraction_leaf=0.0, max_features=None, random_state=None, max_leaf_nodes=None, min_impurity_decrease=0.0, min_impurity_split=None, presort=False)

    A decision tree regressor.

    Read more in the User Guide.

2015

• http://scikit-learn.org/stable/modules/tree.html#regression
  • Decision trees can also be applied to regression problems, using the DecisionTreeRegressor class.

    As in the classification setting, the fit method will take as argument arrays X and y, only that in this case y is expected to have floating point values instead of integer values:

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