Developer Guide

Contents

Batch Processing

Gradient boosted trees classification follows the general workflow described in Training and Prediction > Regression > Usage Model and Training and Prediction > Classification and Regression > Gradient Boosted Trees.

Training

For the description of the input and output, refer to Training and Prediction > Regression > Usage Model. In addition to parameters of the gradient boosted trees described in Training and Prediction > Classification and Regression > Gradient Boosted Trees > Batch Processing, the gradient boosted trees 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 gradient boosted trees regression. The only training method supported so far is the default dense method.
nClasses
Not applicable.
The number of classes. A required parameter.
loss
crossEntropy
Loss function type.

Prediction

For the description of the input and output, refer to Usage Model: Training and Prediction.
In addition to the parameters of the classifier, the gradient boosted trees classifier 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 gradient boosted trees regression. The only training method supported so far is the default dense method.
nClasses
Not applicable.
The number of classes. A required parameter.
numIterations
0
An integer parameter that indicates how many trained iterations of the model should be used in prediction. The default value 0 denotes no limit. All the trained trees should be used.
resultsToEvaluate
computeClassLabels
The 64-bit integer flag that specifies which extra characteristics of the decision forest prediction should be computed.
Provide one of the following values to request a single characteristic or use bitwise OR to request a combination of the characteristics:
  • computeClassLabels
    for prediction of labels of classes. The result contains a numeric table of size
    nSamples
    x
    nClasses
    .
  • computeClassProbabilities
    for probabilities to belong to each class. The result contains a numeric table of size
    nSamples
    x
    nClasses
    .

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