Developer Guide for Intel® Data Analytics Acceleration Library 2018 Update 3
Decision forest classification follows the general workflow described in Training and Prediction > Classification > Usage Model and Training and Prediction > Classification and Regression > Decision Forest.
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. |
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.
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. |