Developer Guide

Contents

Batch Processing

Training

In addition to the parameters of classifier and decision forest described in Training and Prediction > Classification > Usage Model and Classification and Regression > Decision Forest > Batch Processing, the decision forest classification training algorithm 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 forest classification. The only prediction method supported so far is the default dense method.
nClasses
Not applicable.
The number of classes. A required parameter.
votingMethod
weighted
A flag that specifies which method is used to compute probabilities and class labels:
  • weighted
    • Probability for each class is computed as a sample mean of estimates across all trees, where each estimate is the normalized number of training samples for this class that were recorded in a particular leaf node for current input.
    • The algorithm returns the label for the class that gets the maximal value in a sample mean.
  • unweighted
    • Probabilities are computed as normalized votes distribution across all trees of the forest.
    • The algorithm returns the label for the class that gets the majority of votes across all trees of the forest.

Output

Decision forest classification calculates the result of regression and decision forest. For more details, refer to Training and Prediction > Classification > Usage Model, Classification and Regression > Decision Forest > Batch Processing.

Prediction

For the description of the input and output, refer to Training and Prediction > Classification > Usage Model.
In addition to the parameters of the classifier, decision forest classification has the following parameters at the prediction stage:
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 forest classification. The only prediction method supported so far is the default dense method.
nClasses
Not applicable.
The number of classes. A required parameter.

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