Intel® oneAPI Data Analytics Library Developer Guide and Reference
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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: