evmetrics-ml: Multi-label evaluation metrics

In mldr: Exploratory Data Analysis and Manipulation of Multi-Label Data Sets

Description

Several evaluation metrics designed for multi-label problems.

Usage

hamming_loss(true_labels, predicted_labels)

subset_accuracy(true_labels, predicted_labels)

Arguments

true_labels	Matrix of true labels, columns corresponding to labels and rows to instances.
predicted_labels	Matrix of predicted labels, columns corresponding to labels and rows to instances.

Details

Available metrics in this category

• hamming_loss: describes the average absolute distance between a predicted label and its true value.

• subset_accuracy: the ratio of correctly predicted labelsets.

Value

Resulting value in the range [0, 1]

See Also

mldr_evaluate, mldr_to_labels

Other evaluation metrics: Averaged metrics, Ranking-based metrics

Examples

true_labels <- matrix(c(
1,1,1,
0,0,0,
1,0,0,
1,1,1,
0,0,0,
1,0,0
), ncol = 3, byrow = TRUE)
predicted_labels <- matrix(c(
1,1,1,
0,0,0,
1,0,0,
1,1,0,
1,0,0,
0,1,0
), ncol = 3, byrow = TRUE)

hamming_loss(true_labels, predicted_labels)
subset_accuracy(true_labels, predicted_labels)

Example output

[1] 0.2222222
[1] 0.5

mldr documentation built on Jan. 11, 2020, 9:18 a.m.