lcard_threshold: Threshold based on cardinality

In utiml: Utilities for Multi-Label Learning

Description

Find and apply the best threshold based on cardinality of training set. The threshold is choice based on how much the average observed label cardinality is close to the average predicted label cardinality.

Usage

lcard_threshold(prediction, cardinality, probability = FALSE)

## Default S3 method:
lcard_threshold(prediction, cardinality, probability = FALSE)

## S3 method for class 'mlresult'
lcard_threshold(prediction, cardinality, probability = FALSE)

Arguments

prediction	A matrix or mlresult.
cardinality	A real value of training dataset label cardinality, used to define the threshold value.
probability	A logical value. If TRUE the predicted values are the score between 0 and 1, otherwise the values are bipartition 0 or 1. (Default: FALSE)

Value

A mlresult object.

Methods (by class)

• default: Cardinality Threshold for matrix or data.frame

• mlresult: Cardinality Threshold for mlresult

References

Read, J., Pfahringer, B., Holmes, G., & Frank, E. (2011). Classifier chains for multi-label classification. Machine Learning, 85(3), 333-359.

See Also

Other threshold: fixed_threshold(), mcut_threshold(), pcut_threshold(), rcut_threshold(), scut_threshold(), subset_correction()

Examples

prediction <- matrix(runif(16), ncol = 4)
lcard_threshold(prediction, 2.1)

Example output

Loading required package: mldr
     [,1] [,2] [,3] [,4]
[1,]    0    0    1    1
[2,]    1    0    1    1
[3,]    1    0    0    0
[4,]    0    1    0    1

utiml documentation built on May 31, 2021, 9:09 a.m.