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 | 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:
|
with predicted labels
with probabilities to belong to each classExamples
C++ (CPU)
Batch Processing:
Java*
There is no support for Java on GPU.
Batch Processing:
Python*
Batch Processing: