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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

C++ (CPU)Python*

Batch Processing:

• stump_cls_gini_dense_batch.cpp

• stump_cls_infogain_dense_batch.cpp

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