roc.curve.factor.md

roc.curve.factor	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 preventR-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 n, and k levels. Can be of integer or factor.
response	A n \times k <double>-matrix of predicted probabilities. The i-th row should sum to 1 (i.e., a valid probability distribution over the k classes). The first column corresponds to the first factor level in actual, the second column to the second factor level, and so on.
thresholds	An optional <double> vector of length n (default: NULL).
indices	An optional n \times k matrix of <integer> values of sorted response probability indices.
...	Arguments passed into other methods.

Value

A data.frame on the following form,

threshold	<numeric> Thresholds used to determine tpr() and fpr()
level	<character> The level of the actual <factor>
label	<character> The levels of the actual <factor>
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.

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
 )
)

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