Developer Guide for Intel® Data Analytics Acceleration Library 2019 Update 4
Gradient boosted trees regression follows the general workflow described in Training and Prediction > Regression > Usage Model and Training and Prediction > Classification and Regression > Gradient Boosted Trees.
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 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 gradient boosted trees regression. The only training method supported so far is the default dense method. |
|
loss |
squared |
Loss function type. |
For the description of the input and output, refer to Training and Prediction > Regression > Usage Model.
In addition to the parameters of regression, the gradient boosted trees 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 gradient boosted trees regression. The only training method supported so far is the default dense method. |
|
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. |