Bayes' Theorem to Confusion Matrix & Precision-FPR Plotter of maXbox5

1. Model & Population Parameters

Total Population:

Class Prevalence P(A) [0..1]:

Recall / Sensitivity (TPR) [0..1]:

False Positive Rate (FPR): 5.0%

2. Empirical Confusion Matrix Counts

	Predicted Positive (+)	Predicted Negative (-)	Total Actual
Actual Positive (A)	90 (TP)	10 (FN)	100
Actual Negative (¬A)	495 (FP)	9405 (TN)	9900
Total Predicted	585	9415	10000

Bayesian Precision P(A|+): 15.38%

3. Precision vs. FPR Curve Plotter

The Precision Paradox: At 1.0% prevalence, 495 false positives dwarf 90 true positives, causing precision to drop to 15.38% despite 90% recall and a low 5% FPR.