pcut_threshold: Proportional Thresholding (PCut)

Description Usage Arguments Value Methods (by class) References See Also Examples

Description

Define the proportion of examples for each label will be positive. The Proportion Cut (PCut) method can be a label-wise or global method that calibrates the threshold(s) from the training data globally or per label.

Usage

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pcut_threshold(prediction, ratio, probability = FALSE)

## Default S3 method:
pcut_threshold(prediction, ratio, probability = FALSE)

## S3 method for class 'mlresult'
pcut_threshold(prediction, ratio, probability = FALSE)

Arguments

prediction

A matrix or mlresult.

ratio

A single value between 0 and 1 or a list with ratio values contained one value per label.

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: Proportional Thresholding (PCut) method for matrix

  • mlresult: Proportional Thresholding (PCut) 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.

Largeron, C., Moulin, C., & Gery, M. (2012). MCut: A Thresholding Strategy for Multi-label Classification. In 11th International Symposium, IDA 2012 (pp. 172-183).

See Also

Other threshold: fixed_threshold, lcard_threshold, mcut_threshold, rcut_threshold, scut_threshold, subset_correction

Examples

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prediction <- matrix(runif(16), ncol = 4)
pcut_threshold(prediction, .45)


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