Calculate the true positive count from a confusion matrix and classification results.
True positives are the correctly identified positive cases: TP = total_population × prevalence × sensitivity. Prevalence is the fraction of the population that truly has the condition/class, and sensitivity is the fraction of those true cases the test correctly detects. This formula is commonly used to convert population-level statistics (like disease prevalence and test sensitivity from a clinical study) into expected confusion matrix counts for a given population size.
TP = total * prevalence * sensitivity
Sensitivity is a rate (TP / actual positives), while TP count is the absolute number of correctly identified positives in a specific population — you need both prevalence and population size to convert one into the other.
Actual positives is simply total population × prevalence — the number of individuals who truly belong to the positive class, regardless of what the test/model predicts.
Use the FP, TN, and FN Calculators with the same total, prevalence, sensitivity, and specificity inputs to build out the complete 2×2 confusion matrix.