Determines an optimal probability threshold for binary classification based on maximizing F1-score or Youden's J statistic.
Usage
find_optimal_threshold_dia(
prob_positive,
y_true,
type = c("f1", "youden"),
pos_class,
neg_class
)Arguments
- prob_positive
A numeric vector of predicted probabilities for the positive class.
- y_true
A factor vector of true class labels.
- type
A character string, specifying the optimization criterion: "f1" for F1-score or "youden" for Youden's J statistic (Sensitivity + Specificity - 1).
- pos_class
A character string, the label for the positive class.
- neg_class
A character string, the label for the negative class.
Examples
y_true_ex <- factor(c("Negative", "Positive", "Positive", "Negative", "Positive"),
levels = c("Negative", "Positive"))
prob_ex <- c(0.1, 0.8, 0.6, 0.3, 0.9)
# Find threshold maximizing F1-score
opt_f1_threshold <- find_optimal_threshold_dia(
prob_positive = prob_ex,
y_true = y_true_ex,
type = "f1",
pos_class = "Positive",
neg_class = "Negative"
)
print(opt_f1_threshold)
#> [1] 0.6
# Find threshold maximizing Youden's J
opt_youden_threshold <- find_optimal_threshold_dia(
prob_positive = prob_ex,
y_true = y_true_ex,
type = "youden",
pos_class = "Positive",
neg_class = "Negative"
)
print(opt_youden_threshold)
#> [1] 0.6