Developer Guide for Intel® Data Analytics Acceleration Library 2018
Decision forest regression follows the general workflow described in Training and Prediction > Regression > Usage Model and Training and Prediction > Classification and Regression > Decision Forest.
For the description of the input and output, refer to Training and Prediction > Regression > Usage Model. In addition to the parameters of decision forest described in Classification and Regression > Decision Forest > Batch Processing, the decision forest regression 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 regression. The only training method supported so far is the default dense method. |
In addition to the output of regression described in Training and Prediction > Regression > Usage Model, decision forest regression calculates the result of decision forest. For more details, refer to Classification and Regression > Decision Forest > Batch Processing.
For the description of the input and output, refer to Training and Prediction > Regression > Usage Model.
In addition to the parameters of regression, decision forest regression 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 regression. The only training method supported so far is the default dense method. |