Details

Given:

  • n feature vectors x 1=(x 11, ..., x 1p ), ..., x n =(x n1, ..., x np ) of size p
  • The vector of responses y=(y 1, ..., y n), where y i R describes the dependent variable for independent variables x i .

The problem is to build a regression decision tree.

Split Criterion

The library provides the decision tree regression algorithm based on the mean-squared error (MSE) [Breiman84].:

Where

  • O( τ ) is the set of all possible outcomes of test τ

  • D v is the subset of D, for which outcome of τ is v, for example, .

The test used in the node is selected as . For binary decision tree with "true" and "false" branches,

Training Stage

The regression decision tree follows the algorithmic framework of decision tree training described in Classification and Regression > Decision tree >Training stage.

Prediction Stage

The regression decision tree follows the algorithmic framework of decision tree prediction described in Classification and Regression > Decision tree > Prediction stage.

Given the regression decision tree and vectors x 1, …, x r , the problem is to calculate the responses for those vectors.

For more complete information about compiler optimizations, see our Optimization Notice.
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