Visible to Intel only — GUID: GUID-F37B83C3-84BF-485F-9257-A0A45BAC68FD
Visible to Intel only — GUID: GUID-F37B83C3-84BF-485F-9257-A0A45BAC68FD
Classification Stump
A Classification 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. Two methods of split criterion are available: gini and information gain. See Classification Decision Tree for details.
Batch Processing
A classification stump follows the general workflow described in Classification Usage Model.
Training
For a description of the input and output, refer to Classification Usage Model.
At the training stage, a classification 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. |
splitCriterion |
decision_tree::classification::gini |
Split criteria for classification stump. Two split criterion are available:
See Classification Decision Tree chapter for details. |
varImportance |
none |
NOTE:
Variable importance computation is not supported for current version of the library.
|
nClasses |
2 |
The number of classes. |
Prediction
For a description of the input and output, refer to Classification Usage Model.
At the prediction stage, a classification 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. |
nClasses |
2 |
The number of classes. |
resultsToEvaluate |
classifier::computeClassLabels |
The form of computed result:
|
Examples
C++ (CPU)
Batch Processing:
Python*
Batch Processing: