Developer Guide and Reference

  • 2021.1
  • 12/04/2020
  • 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:
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
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:
  • 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

Java*
There is no support for Java on GPU.
Batch Processing:
Python*

Product and Performance Information

1

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