Difference between revisions of "sklearn.tree.DecisionTreeRegressor"

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A [[sklearn.tree.DecisionTreeRegressor]] is a [[regression tree learning system]] within <code>[[sklearn.tree]]</code>.
 
A [[sklearn.tree.DecisionTreeRegressor]] is a [[regression tree learning system]] within <code>[[sklearn.tree]]</code>.
* <B>AKA:</B> [[tree.DecisionTreeRegressor]], [[sklearn.tree.DecisionTreeRegressor|DecisionTreeRegressor]]
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* <B>AKA:</B> [[sklearn.tree.DecisionTreeRegressor|tree.DecisionTreeRegressor]], [[sklearn.tree.DecisionTreeRegressor|DecisionTreeRegressor]]
 
* <B>Context</B>
 
* <B>Context</B>
 
** Usage:   
 
** Usage:   

Latest revision as of 20:45, 23 December 2019

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

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.


References

2017

2015