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

Note

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

Batch Processing:

stump_reg_mse_dense_batch.cpp

Note

There is no support for Java on GPU.

Batch Processing:

StumpRegMseDenseBatch.java

Batch Processing: