Compute measures of agreement between observed and predicted responses.
accuracy(observed, predicted = NULL, cutoff = 0.5, ...)auc(observed, predicted = NULL, metrics = c(MachineShop::tpr,
MachineShop::fpr), stat = base::mean, ...)
brier(observed, predicted = NULL, ...)
cindex(observed, predicted = NULL, ...)
cross_entropy(observed, predicted = NULL, ...)
f_score(observed, predicted = NULL, cutoff = 0.5, beta = 1, ...)
fnr(observed, predicted = NULL, cutoff = 0.5, ...)
fpr(observed, predicted = NULL, cutoff = 0.5, ...)
kappa2(observed, predicted = NULL, cutoff = 0.5, ...)
npv(observed, predicted = NULL, cutoff = 0.5, ...)
ppv(observed, predicted = NULL, cutoff = 0.5, ...)
pr_auc(observed, predicted = NULL, ...)
precision(observed, predicted = NULL, cutoff = 0.5, ...)
recall(observed, predicted = NULL, cutoff = 0.5, ...)
roc_auc(observed, predicted = NULL, ...)
roc_index(observed, predicted = NULL, cutoff = 0.5,
f = function(sensitivity, specificity) (sensitivity + specificity)/2,
...)
rpp(observed, predicted = NULL, cutoff = 0.5, ...)
sensitivity(observed, predicted = NULL, cutoff = 0.5, ...)
specificity(observed, predicted = NULL, cutoff = 0.5, ...)
tnr(observed, predicted = NULL, cutoff = 0.5, ...)
tpr(observed, predicted = NULL, cutoff = 0.5, ...)
weighted_kappa2(observed, predicted = NULL, power = 1, ...)
gini(observed, predicted = NULL, ...)
mae(observed, predicted = NULL, ...)
mse(observed, predicted = NULL, ...)
msle(observed, predicted = NULL, ...)
r2(observed, predicted = NULL, dist = NULL, ...)
rmse(observed, predicted = NULL, ...)
rmsle(observed, predicted = NULL, ...)
observed responses, Curves object, or
ConfusionMatrix of observed and predicted
responses.
predicted responses.
threshold above which binary factor probabilities are classified as events and below which survival probabilities are classified.
arguments passed to or from other methods.
list of two performance metrics for the calculation [default: ROC metrics].
function to compute a summary statistic at each cutoff value of
resampled metrics in Curves, or NULL for resample-specific
metrics.
relative importance of recall to precision in the calculation of
f_score [default: F1 score].
function to calculate a desired sensitivity-specificity tradeoff.
power to which positional distances of off-diagonals from the
main diagonal in confusion matrices are raised to calculate
weighted_kappa2.
character string specifying a distribution with which to estimate
the survival mean in the total sum of square component of r2.
Possible values are "empirical" for the Kaplan-Meier estimator,
"exponential", "extreme", "gaussian",
"loggaussian", "logistic", "loglogistic",
"lognormal", "rayleigh", "t", or "weibull"
(default).