Developer Guide and Reference

  • 2021.6
  • 04/11/2022
  • Public Content
Contents

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:
Training Parameters for Classification Stump (Batch Processing)
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:
  • decision_tree::classification::gini
  • decision_tree::classification::infoGain
See Classification Decision Tree chapter for details.
varImportance
none
Variable importance computation is not supported for current version of the library.
nClasses
LaTex Math image.
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:
Training Parameters for Classification Stump (Batch Processing)
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
LaTex Math image.
The number of classes.
resultsToEvaluate
classifier::computeClassLabels
The form of computed result:
  • classifier::computeClassLabels
    – the result contains the
    NumericTable
    of size LaTex Math image. with predicted labels
  • classifier::computeClassProbabilities
    – the result contains the
    NumericTable
    of size LaTex Math image. with probabilities to belong to each class

Examples

C++ (CPU)
Batch Processing:
Java*
There is no support for Java on GPU.
Batch Processing:
Python*
Batch Processing:

Product and Performance Information

1

Performance varies by use, configuration and other factors. Learn more at www.Intel.com/PerformanceIndex.