cmatrix.factor {SLmetrics} | R Documentation |
Confusion Matrix
Description
A generic S3 function to compute the confusion matrix for a classification model. This function dispatches to S3 methods in cmatrix()
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 cmatrix()
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 cmatrix()
in a "safe" validator that checks for NA values and matching length, for example:
safe_cmatrix <- function(x, y, ...) { stopifnot( !anyNA(x), !anyNA(y), length(x) == length(y) ) cmatrix(x, y, ...) }
Apply the same pattern to any custom metric functions to ensure input sanity before calling the underlying C++
code.
The workhorse
cmatrix()
is the main function for classification metrics with cmatrix S3 dispatch. These functions internally calls cmatrix()
, so there is a signficant gain in computing the confusion matrix first, and then pass it onto the metrics.
For example:
## Compute confusion matrix confusion_matrix <- cmatrix(actual, predicted) ## Evaluate accuracy ## via S3 dispatching accuracy(confusion_matrix) ## Evaluate recall ## via S3 dispatching recall(confusion_matrix)
Usage
## S3 method for class 'factor'
cmatrix(actual, predicted, ...)
Arguments
actual , predicted |
A pair of <integer> or <factor> vectors of length |
... |
Arguments passed into other methods. |
Value
A named k
x k
<matrix>
Dimensions
There is no robust defensive measure against misspecifying the confusion matrix. If the arguments are passed correctly, the resulting confusion matrix is on the form:
A (Predicted) | B (Predicted) | |
A (Actual) | Value | Value |
B (Actual) | Value | 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.
See Also
Other Classification:
accuracy()
,
auc.pr.curve()
,
auc.roc.curve()
,
baccuracy()
,
brier.score()
,
ckappa()
,
cross.entropy()
,
dor()
,
fbeta()
,
fdr()
,
fer()
,
fmi()
,
fpr()
,
hammingloss()
,
jaccard()
,
logloss()
,
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()
,
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()
,
roc.curve()
,
rrmse()
,
rrse()
,
rsq()
,
shannon.entropy()
,
smape()
,
specificity()
,
zerooneloss()
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")
)
## Compute confusion matrix
SLmetrics::cmatrix(
actual = actual_classes,
predicted = predicted_classes
)