Calculate Classification Metrics at a Specific Threshold

Calculates various classification performance metrics (Accuracy, Precision, Recall, F1-score, Specificity, True Positives, etc.) for binary classification at a given probability threshold.

Usage

calculate_metrics_at_threshold_dia(
  prob_positive,
  y_true,
  threshold,
  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.

threshold

A numeric value between 0 and 1, the probability threshold above which a prediction is considered positive.

pos_class

A character string, the label for the positive class.

neg_class

A character string, the label for the negative class.

Value

A list containing:

• Threshold: The threshold used.

• Accuracy: Overall prediction accuracy.

• Precision: Precision for the positive class.

• Recall: Recall (Sensitivity) for the positive class.

• F1: F1-score for the positive class.

• Specificity: Specificity for the negative class.

• TP, TN, FP, FN, N: Counts of True Positives, True Negatives, False Positives, False Negatives, and total samples.

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)
metrics <- calculate_metrics_at_threshold_dia(
  prob_positive = prob_ex,
  y_true = y_true_ex,
  threshold = 0.5,
  pos_class = "Positive",
  neg_class = "Negative"
)
print(metrics)
#> $Threshold
#> [1] 0.5
#> 
#> $Accuracy
#> Accuracy 
#>        1 
#> 
#> $Precision
#> Precision 
#>         1 
#> 
#> $Recall
#> Recall 
#>      1 
#> 
#> $F1
#> F1 
#>  1 
#> 
#> $Specificity
#> Specificity 
#>           1 
#> 
#> $TP
#> [1] 3
#> 
#> $TN
#> [1] 2
#> 
#> $FP
#> [1] 0
#> 
#> $FN
#> [1] 0
#> 
#> $N
#> [1] 5
#>