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Visible to Intel only — GUID: GUID-FE161AE9-F196-417E-8B97-B3EB021CF9F9
Regression Decision Tree
Regression decision tree is a kind of decision trees described in Classification and Regression > Decision Tree.
Details
Given:
n feature vectors of size p
The vector of responses , where describes the dependent variable for independent variables .
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
is the set of all possible outcomes of test
is the subset of D, for which outcome of 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 Decision Tree.
Prediction Stage
The regression decision tree follows the algorithmic framework of decision tree prediction described in Decision Tree.
Given the regression decision tree and vectors , the problem is to calculate the responses for those vectors.
Batch Processing
Decision tree regression follows the general workflow described in Regression Usage Model.
Training
At the training stage, decision tree regression has the following parameters:
Parameter |
Default Value |
Description |
---|---|---|
algorithmFPType |
float |
The floating-point type that the algorithm uses for intermediate computations. Can be float or double. |
method |
defaultDense |
The computation method used by the decision tree regression. The only training method supported so far is the default dense method. |
pruning |
reducedErrorPruning |
Method to perform post-pruning. Available options for the pruning parameter:
|
maxTreeDepth |
0 |
Maximum tree depth. Zero value means unlimited depth. Can be any non-negative number. |
minObservationsInLeafNodes |
5 |
Minimum number of observations in the leaf node. Can be any positive number. |
pruningFraction |
0.2 |
Fraction of observations from training dataset to be used as observations for post-pruning via random sampling. The rest observations (with fraction to be used to build a decision tree). Can be any number in the interval (0, 1). If pruning is not used, all observations are used to build the decision tree regardless of this parameter value. |
engine |
SharedPtr<engines::mt19937::Batch<> >() |
Pointer to the random number engine to be used for random sampling for reduced error post-pruning. |
Prediction
At the prediction stage, decision tree regression has the following parameters:
Parameter |
Default Value |
Description |
---|---|---|
algorithmFPType |
float |
The floating-point type that the algorithm uses for intermediate computations. Can be float or double. |
method |
defaultDense |
The computation method used by the decision tree regression. The only training method supported so far is the default dense method. |
Examples
C++ (CPU)
Batch Processing:
Python*
Batch Processing: