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

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:

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

\(2\)

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#