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