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

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:

  • classifier::computeClassLabels – the result contains the NumericTable of size \(n \times 1\) with predicted labels

  • classifier::computeClassProbabilities – the result contains the NumericTable of size \(n \times \text{nClasses}\) with probabilities to belong to each class

Examples

Note

There is no support for Java on GPU.

Batch Processing:

Batch Processing: