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)
Batch Processing:
Java*
Python*
Batch Processing: