Classification Metrics Explorer

Move prevalence, sensitivity and specificity independently and watch predictive values, accuracy and F1 respond.

sensitivity
specificity
predictive value
prevalence

Ranking metrics (threshold-free)

AUC

0.695

Brier score

0.107

Log loss

0.357

Prevalence

0.127

Distribution of predicted probabilities

BenignMalignant

Confusion matrix at current threshold

	Pred +	Pred −
True +	0	19
True −	0	131

Threshold-dependent metrics

AUC, Brier score and log loss never move as you drag the slider — they summarize the whole ranking, not one operating point.
Metric	Value	Type
Accuracy	0.873	Threshold-dependent
Sensitivity	0.000	Threshold-dependent
Specificity	1.000	Threshold-dependent
PPV	0.000	Threshold-dependent
NPV	0.873	Threshold-dependent
Youden's J	0.000	Threshold-dependent
AUC	0.695	Ranking / probability
Brier score	0.107	Ranking / probability
Log loss	0.357	Ranking / probability

Why 0.5 is rarely the right threshold

Drag the threshold toward 0.2 and watch sensitivity climb while PPV falls — moving the line only trades one error type for the other along a fixed probability ranking. With prevalence near 0.13, accuracy can look good even from a model that is not clinically useful, which is exactly why predictive values must always be read together with the underlying prevalence.

Preparing the simulation…

Conceptual bridges & related modules

QRS Detection & HRV Analyzer
downstream classifier · Chapter 7
Understand feature-extraction errors before using cardiac features in classification.