Visible to Intel only — GUID: GUID-99B5A7F1-A78A-467C-AA1C-E1F41443770C
Visible to Intel only — GUID: GUID-99B5A7F1-A78A-467C-AA1C-E1F41443770C
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
C++ (CPU)
Batch Processing:
Python*
Batch Processing: