misclassification_cost: Calculate the misclassification cost

In cutpointr: Determine and Evaluate Optimal Cutpoints in Binary Classification Tasks

Calculate the misclassification cost

Description

Calculate the misclassification cost from true positives, false positives, true negatives and false negatives. The inputs must be vectors of equal length.

misclassification_cost = cost_fp * fp + cost_fn * fn

Usage

misclassification_cost(tp, fp, tn, fn, cost_fp = 1, cost_fn = 1, ...)

Arguments

tp	(numeric) number of true positives.
fp	(numeric) number of false positives.
tn	(numeric) number of true negatives.
fn	(numeric) number of false negatives.
cost_fp	(numeric) the cost of a false positive
cost_fn	(numeric) the cost of a false negative
...	for capturing additional arguments passed by method.

See Also

Other metric functions: F1_score(), Jaccard(), abs_d_ppv_npv(), abs_d_sens_spec(), accuracy(), cohens_kappa(), cutpoint(), false_omission_rate(), metric_constrain(), npv(), odds_ratio(), p_chisquared(), plr(), ppv(), precision(), prod_ppv_npv(), prod_sens_spec(), recall(), risk_ratio(), roc01(), sensitivity(), specificity(), sum_ppv_npv(), sum_sens_spec(), total_utility(), tpr(), tp(), youden()

Examples

misclassification_cost(10, 5, 20, 10, cost_fp = 1, cost_fn = 5)
misclassification_cost(c(10, 8), c(5, 7), c(20, 12), c(10, 18),
                       cost_fp = 1, cost_fn = 5)

cutpointr documentation built on April 14, 2022, 1:06 a.m.