roc.curve.factor {SLmetrics} | R Documentation |
Reciever Operator Characteristics
Description
A generic S3 function to compute the reciever operator characteristics score for a classification model. This function dispatches to S3 methods in roc.curve()
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 roc.curve()
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 roc.curve()
in a "safe" validator that checks for NA values and matching length, for example:
safe_roc.curve <- function(x, y, ...) { stopifnot( !anyNA(x), !anyNA(y), length(x) == length(y) ) roc.curve(x, y, ...) }
Apply the same pattern to any custom metric functions to ensure input sanity before calling the underlying C++
code.
Area under the curve
Use auc.roc.curve for calculating the area under the curve directly.
Efficient multi-metric evaluation
To avoid sorting the same probability matrix multiple times (once per class or curve), you can precompute a single set of sort indices and pass it via the indices
argument. This reduces the overall cost from O(K·N log N) to O(N log N + K·N).
## presort response ## probabilities indices <- preorder(response, decreasing = TRUE) ## evaluate reciever operator characteristics roc.curve(actual, response, indices = indices)
Usage
## S3 method for class 'factor'
roc.curve(actual, response, thresholds = NULL, indices = NULL, ...)
Arguments
actual |
A vector length |
response |
A |
thresholds |
|
indices |
An optional |
... |
Arguments passed into other methods. |
Value
A data.frame on the following form,
threshold |
|
level |
|
label |
|
fpr |
<numeric> The false positive rate |
tpr |
<numeric> The true positve rate |
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.
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()
,
logloss()
,
mcc()
,
nlr()
,
npv()
,
plr()
,
pr.curve()
,
precision()
,
recall()
,
relative.entropy()
,
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()
,
logloss()
,
maape()
,
mae()
,
mape()
,
mcc()
,
mpe()
,
mse()
,
nlr()
,
npv()
,
pinball()
,
plr()
,
pr.curve()
,
precision()
,
rae()
,
recall()
,
relative.entropy()
,
rmse()
,
rmsle()
,
rrmse()
,
rrse()
,
rsq()
,
shannon.entropy()
,
smape()
,
specificity()
,
zerooneloss()
Examples
## Classes and
## seed
set.seed(1903)
classes <- c("Kebab", "Falafel")
## Generate actual classes
## and response probabilities
actual_classes <- factor(
x = sample(
x = classes,
size = 1e2,
replace = TRUE,
prob = c(0.7, 0.3)
)
)
response_probabilities <- ifelse(
actual_classes == "Kebab",
rbeta(sum(actual_classes == "Kebab"), 2, 5),
rbeta(sum(actual_classes == "Falafel"), 5, 2)
)
## Construct response
## matrix
probability_matrix <- cbind(
response_probabilities,
1 - response_probabilities
)
## Visualize
plot(
SLmetrics::roc.curve(
actual = actual_classes,
response = probability_matrix
)
)