Developer Guide for Intel® Data Analytics Acceleration Library 2019 Update 2

Batch Processing

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

Training

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.

Output

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.

Prediction

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.

Examples

C++: df_reg_dense_batch.cpp

Java*: DfRegDenseBatch.java

Python*: df_reg_dense_batch.py

See Also