Bayesian Calculator

// maXbox5 / maXbox6 Script: Bayesian Precision Conversion Calculator // Demonstrates how Bayes' Theorem calculates Precision from Recall, FPR, and Prevalence Tutor: 3  program Bayes_Precision_Conversion; var TP, FN, FP, TN, Total: Double; Recall, FPR, Prevalence, OneMinusPrev: Double; BayesNum, BayesDenom, BayesPrecision: Double; EmpiricalPrecision: Double; procedure CalculateBayesPrecision; begin // Given Confusion Matrix Counts: TP = 6, FN = 4, FP = 8, TN = 3  TP:= 6.0; FN:= 4.0; FP:= 8.0; TN:= 3.0; Total:= TP + FN + FP + TN; // 21  // 1. Derived Bayesian Parameters  Recall:= TP / (TP + FN); // 6 / 10 = 0.6000 (60.00%)  FPR:= FP / (FP + TN); // 8 / 11 = 0.7273 (72.73%)  Prevalence:= (TP + FN) / Total; // 10 / 21 = 0.4762 (47.62%)  OneMinusPrev:= 1.0 - Prevalence; // 11 / 21 = 0.5238 (52.38%)  // 2. Bayesian Precision Formula:  // Precision = (Recall * Prevalence) / [ (Recall * Prevalence) + (FPR * (1 - Prevalence)) ]  BayesNum:= Recall * Prevalence; // (6/10) * (10/21) = 6/21 = 0.2857  BayesDenom:= BayesNum + (FPR * OneMinusPrev); // (6/21) + (8/21) = 14/21 = 0.6667  BayesPrecision:= (BayesNum / BayesDenom) * 100.0; // (6/14) * 100 = 42.86%  // 3. Empirical Precision Verification: TP / (TP + FP)  EmpiricalPrecision:= (TP / (TP + FP)) * 100.0; // 6 / 14 = 42.86%  // 4. Output Results to Console  Writeln('================================================================'); Writeln(' BAYESIAN PRECISION CONVERSION CALCULATOR (maXbox)'); Writeln('================================================================'); Writeln('1. CONFUSION MATRIX INPUTS:'); Writeln(' True Positives (TP) : ' + FloatToStr(TP)); Writeln(' False Negatives (FN) : ' + FloatToStr(FN)); Writeln(' False Positives (FP) : ' + FloatToStr(FP)); Writeln(' True Negatives (TN) : ' + FloatToStr(TN)); Writeln(' Total Population : ' + FloatToStr(Total)); Writeln(''); Writeln('2. DERIVED BAYESIAN PARAMETERS:'); Writeln(' Recall (TPR) : ' + FormatFloat('0.00%', Recall * 100.0) + ' [6/10]'); Writeln(' False Positive Rate : ' + FormatFloat('0.00%', FPR * 100.0) + ' [8/11]'); Writeln(' Class Prevalence : ' + FormatFloat('0.00%', Prevalence * 100.0) + ' [10/21]'); Writeln(' 1 - Prevalence : ' + FormatFloat('0.00%', OneMinusPrev * 100.0) + ' [11/21]'); Writeln(''); Writeln('3. BAYESIAN FORMULA STEP-BY-STEP:'); Writeln(' Numerator (Rec * Prev) : ' + FormatFloat('0.0000', BayesNum) + ' [6/21]'); Writeln(' Denominator : ' + FormatFloat('0.0000', BayesDenom) + ' [14/21]'); Writeln(''); Writeln('4. PRECISION RESULT COMPARISON:'); Writeln(' Bayesian Precision : '+ FormatFloat('0.00%', BayesPrecision) +' [3/7 = 42.86%]'); Writeln(' Empirical Precision : '+ FormatFloat('0.00%', EmpiricalPrecision)+' [6/14 = 42.86%]'); Writeln('================================================================'); end; begin //@main setDebugOff; maxform1.setconsole; //setConsole  memo2.font.name:= 'Courier'; memo2.font.size:= 10; CalculateBayesPrecision; end.