baccuracy.md
baccuracy
R Documentation
Balanced Accuracy
Description
A generic S3 function to compute the balanced accuracy score for a
classification model. This function dispatches to S3 methods inbaccuracy()
and performs no input validation. If you supply NA values
or vectors of unequal length (e.g. length(x) != length(y)
), the
underlying C++
code may trigger undefined behavior and crash your R
session.
Defensive measures
Because baccuracy()
operates on raw pointers, pointer-level faults
(e.g. from NA or mismatched length) occur before any R
-level error
handling. Wrapping calls in try()
or tryCatch()
will not preventR
-session crashes.
To guard against this, wrap baccuracy()
in a "safe" validator that
checks for NA values and matching length, for example:
safe_baccuracy <- function(x, y, ...) {
stopifnot(
!anyNA(x), !anyNA(y),
length(x) == length(y)
)
baccuracy(x, y, ...)
}
Apply the same pattern to any custom metric functions to ensure input
sanity before calling the underlying C++
code.
Efficient multi-metric evaluation
For multiple performance evaluations of a classification model, first
compute the confusion matrix once via cmatrix()
. All other performance
metrics can then be derived from this one object via S3 dispatching:
## compute confusion matrix
confusion_matrix <- cmatrix(actual, predicted)
## evaluate balanced accuracy
## via S3 dispatching
baccuracy(confusion_matrix)
## additional performance metrics
## below
The baccuracy.factor()
method calls cmatrix()
internally, so
explicitly invoking baccuracy.cmatrix()
yourself avoids duplicate
computation, yielding significant speed and memory effciency gains when
you need multiple evaluation metrics.
Usage
## Generic S3 method
## for Balanced Accuracy
baccuracy(...)
## Generic S3 method
## for weighted Balanced Accuracy
weighted.baccuracy(...)
Arguments
...
Arguments passed on to baccuracy.factor
,weighted.baccuracy.factor
,baccuracy.cmatrix
adjust
A <logical> value (default: FALSE). If TRUE the metric is
adjusted for random chance \frac{1}{k}
.
actual,predicted
A pair of <integer> or <factor> vectors of length n
, and k
levels.
na.rm
A <logical> value of length 1
(default: TRUE). If TRUE, NA values are removed from the computation.
This argument is only relevant when micro != NULL
. Whenna.rm = TRUE
, the computation corresponds tosum(c(1, 2, NA), na.rm = TRUE) / length(na.omit(c(1, 2, NA)))
.
When na.rm = FALSE
, the computation corresponds tosum(c(1, 2, NA), na.rm = TRUE) / length(c(1, 2, NA))
.
w
A <double> vector of sample weights.
x
A confusion matrix created cmatrix()
.
Value
A <double>-value
References
James, Gareth, et al. An introduction to statistical learning. Vol. 112. No. 1. New York: springer, 2013.
Hastie, Trevor. "The elements of statistical learning: data mining, inference, and prediction." (2009).
Pedregosa, Fabian, et al. "Scikit-learn: Machine learning in Python." the Journal of machine Learning research 12 (2011): 2825-2830.
Examples
## Classes and
## seed
set.seed(1903)
classes <- c("Kebab", "Falafel")
## Generate actual
## and predicted classes
actual_classes <- factor(
x = sample(x = classes, size = 1e3, replace = TRUE),
levels = c("Kebab", "Falafel")
)
predicted_classes <- factor(
x = sample(x = classes, size = 1e3, replace = TRUE),
levels = c("Kebab", "Falafel")
)
## Evaluate performance
SLmetrics::baccuracy(
actual = actual_classes,
predicted = predicted_classes
)
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