Developer Guide

Contents

Batch Processing

Decision tree regression follows the general workflow described in Training and Prediction > Regression > Usage Model.

Training

For the description of the input and output, refer to Training and Prediction > Regression > Usage Model.
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:
  • reducedErrorPruning
    - reduced error pruning. Provide
    dataForPruning
    and
    dependentVariablesForPruning
    inputs, if you use pruning.
  • none
    - do not prune.
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 1-
pruningFraction
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

For the description of the input and output, refer to Training and Prediction > Regression > Usage Model.
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.

Product and Performance Information

1

Intel's compilers may or may not optimize to the same degree for non-Intel microprocessors for optimizations that are not unique to Intel microprocessors. These optimizations include SSE2, SSE3, and SSSE3 instruction sets and other optimizations. Intel does not guarantee the availability, functionality, or effectiveness of any optimization on microprocessors not manufactured by Intel. Microprocessor-dependent optimizations in this product are intended for use with Intel microprocessors. Certain optimizations not specific to Intel microarchitecture are reserved for Intel microprocessors. Please refer to the applicable product User and Reference Guides for more information regarding the specific instruction sets covered by this notice.

Notice revision #20110804