mlr3measures: bacc – R documentation

Become an expert in R — Interactive courses, Cheat Sheets, certificates and more!

bacc

Balanced Accuracy

Description

Computes the weighted balanced accuracy, suitable for imbalanced data sets. It is defined analogously to the definition in sklearn.

First, the sample weights w are normalized per class:

w_hat[i] = w[i] / sum((t == t[i]) * w[i]).

The balanced accuracy is calculated as

1 / sum(w_hat) * sum((r == t) * w_hat).

Usage

bacc(truth, response, sample_weights = NULL, ...)

Arguments

`truth`	(`factor()`) True (observed) labels. Must have the same levels and length as `response`.
`response`	(`factor()`) Predicted response labels. Must have the same levels and length as `truth`.
`sample_weights`	(`numeric()`) Non-negative sample weights. Must have the same levels and length as `truth`. Defaults to equal sample weights.
`...`	(`any`) Additional arguments. Currently ignored.

Value

Performance value as numeric(1).

Meta Information

Type: "classif"
Range: [0, 1]
Minimize: FALSE
Required prediction: response

References

Brodersen KH, Ong CS, Stephan KE, Buhmann JM (2010). “The Balanced Accuracy and Its Posterior Distribution.” In 2010 20th International Conference on Pattern Recognition. doi: 10.1109/icpr.2010.764.

Guyon I, Bennett K, Cawley G, Escalante HJ, Escalera S, Ho TK, Macia N, Ray B, Saeed M, Statnikov A, Viegas E (2015). “Design of the 2015 ChaLearn AutoML challenge.” In 2015 International Joint Conference on Neural Networks (IJCNN). doi: 10.1109/ijcnn.2015.7280767.

Examples

set.seed(1)
lvls = c("a", "b", "c")
truth = factor(sample(lvls, 10, replace = TRUE), levels = lvls)
response = factor(sample(lvls, 10, replace = TRUE), levels = lvls)
bacc(truth, response)

mlr3measures

Performance Measures for 'mlr3'

v0.3.1

LGPL-3

Authors

Michel Lang [cre, aut] (<https://orcid.org/0000-0001-9754-0393>), Martin Binder [ctb]

Initial release