logloss.factor {SLmetrics} | R Documentation |
Logarithmic Loss
Description
A generic S3 function to compute the logarithmic loss score for a classification model. This function dispatches to S3 methods in logloss()
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 logloss()
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 prevent R
-session crashes.
To guard against this, wrap logloss()
in a "safe" validator that checks for NA values and matching length, for example:
safe_logloss <- function(x, y, ...) { stopifnot( !anyNA(x), !anyNA(y), length(x) == length(y) ) logloss(x, y, ...) }
Apply the same pattern to any custom metric functions to ensure input sanity before calling the underlying C++
code.
Usage
## S3 method for class 'factor'
logloss(actual, response, normalize = TRUE, ...)
Arguments
actual |
A vector length |
response |
A |
normalize |
A <logical>-value (default: TRUE). If TRUE, the mean cross-entropy across all observations is returned; otherwise, the sum of cross-entropies is returned. |
... |
Arguments passed into other methods. |
Value
A <double>
References
MacKay, David JC. Information theory, inference and learning algorithms. Cambridge university press, 2003.
Kramer, Oliver, and Oliver Kramer. "Scikit-learn." Machine learning for evolution strategies (2016): 45-53.
Virtanen, Pauli, et al. "SciPy 1.0: fundamental algorithms for scientific computing in Python." Nature methods 17.3 (2020): 261-272.
See Also
Other Classification:
accuracy()
,
auc.pr.curve()
,
auc.roc.curve()
,
baccuracy()
,
brier.score()
,
ckappa()
,
cmatrix()
,
cross.entropy()
,
dor()
,
fbeta()
,
fdr()
,
fer()
,
fmi()
,
fpr()
,
hammingloss()
,
jaccard()
,
mcc()
,
nlr()
,
npv()
,
plr()
,
pr.curve()
,
precision()
,
recall()
,
relative.entropy()
,
roc.curve()
,
shannon.entropy()
,
specificity()
,
zerooneloss()
Other Supervised Learning:
accuracy()
,
auc.pr.curve()
,
auc.roc.curve()
,
baccuracy()
,
brier.score()
,
ccc()
,
ckappa()
,
cmatrix()
,
cross.entropy()
,
deviance.gamma()
,
deviance.poisson()
,
deviance.tweedie()
,
dor()
,
fbeta()
,
fdr()
,
fer()
,
fmi()
,
fpr()
,
gmse()
,
hammingloss()
,
huberloss()
,
jaccard()
,
maape()
,
mae()
,
mape()
,
mcc()
,
mpe()
,
mse()
,
nlr()
,
npv()
,
pinball()
,
plr()
,
pr.curve()
,
precision()
,
rae()
,
recall()
,
relative.entropy()
,
rmse()
,
rmsle()
,
roc.curve()
,
rrmse()
,
rrse()
,
rsq()
,
shannon.entropy()
,
smape()
,
specificity()
,
zerooneloss()
Other Entropy:
cross.entropy()
,
relative.entropy()
,
shannon.entropy()
Examples
## Classes and
## seed
set.seed(1903)
classes <- c("Kebab", "Falafel")
## Generate actual
## and predicted response
## probabilities
actual_classes <- factor(
x = sample(x = classes, size = 1e3, replace = TRUE),
levels = c("Kebab", "Falafel")
)
response <- runif(n = 1e3)
## Evaluate performance
SLmetrics::logloss(
actual = actual_classes,
response = cbind(
response,
1 - response
)
)
## Generate observed
## frequencies
actual_frequency <- sample(10L:100L, size = 1e3, replace = TRUE)
SLmetrics::logloss(
actual = actual_frequency,
response = response
)