rcut_threshold: Rank Cut (RCut) threshold method

In utiml: Utilities for Multi-Label Learning

Description

The Rank Cut (RCut) method is an instance-wise strategy, which outputs the k labels with the highest scores for each instance at the deployment.

Usage

rcut_threshold(prediction, k, probability = FALSE)

## Default S3 method:
rcut_threshold(prediction, k, probability = FALSE)

## S3 method for class 'mlresult'
rcut_threshold(prediction, k, probability = FALSE)

Arguments

prediction	A matrix or mlresult.
k	The number of elements that will be positive.
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: Rank Cut (RCut) threshold method for matrix

• mlresult: Rank Cut (RCut) threshold method for mlresult

References

Al-Otaibi, R., Flach, P., & Kull, M. (2014). Multi-label Classification: A Comparative Study on Threshold Selection Methods. In First International Workshop on Learning over Multiple Contexts (LMCE) at ECML-PKDD 2014.

See Also

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

Examples

prediction <- matrix(runif(16), ncol = 4)
rcut_threshold(prediction, 2)

Example output

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

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