# 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¶

Batch Processing:

Note

There is no support for Java on GPU.

Batch Processing:

Batch Processing: