Developer Guide and Reference

  • 2021.1
  • 12/04/2020
  • Public Content
Contents

Regression Stump

A Regression Decision Stump is a model that consists of a one-level decision tree where the root is connected to terminal nodes (leaves) [Friedman2017]. The library only supports stumps with two leaves based on regression decision trees. The one method of split criteria is available: mse. See Regression Decision Tree for details.

Batch Processing

A regression stump follows the general workflow described in Regression Usage Model.
Training
For a description of the input and output, refer to Regression Usage Model.
At the training stage, a regression decision stump 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
Performance-oriented computation method, the only method supported by the algorithm.
varImportance
none
Variable importance computation is not supported for current version of the library.
Prediction
For a description of the input and output, refer to Regression Usage Model.
At the prediction stage, a regression stump 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
Performance-oriented computation method, the only method supported by the algorithm.

Examples

C++ (CPU)
Java*
There is no support for Java on GPU.
Batch Processing:
Python*
Batch Processing:

Product and Performance Information

1

Performance varies by use, configuration and other factors. Learn more at www.Intel.com/PerformanceIndex.