Classification Accuracy Calculator

Classification accuracy measures the share of all predictions that a model classified correctly. It combines true positives and true negatives, then divides that sum by every observation in the confusion matrix. Accuracy is intuitive, but it should rarely be interpreted alone. When classes are imbalanced, a model can predict the common class almost every time and still report a high accuracy while failing on the cases that matter most. This calculator therefore reports five complementary binary-classification metrics. Precision is the proportion of predicted positives that are genuinely positive, calculated as TP divided by TP plus FP. Recall, also called sensitivity, is the proportion of actual positives found by the model, calculated as TP divided by TP plus FN. Specificity measures how well the model identifies negatives: TN divided by TN plus FP. The F1 score is the harmonic mean of precision and recall, so it rewards models that balance both rather than maximizing one at the expense of the other. Begin by translating your evaluation results into a confusion matrix. A true positive is a positive case correctly predicted positive. A false positive is a negative case incorrectly predicted positive. A true negative is a negative case correctly predicted negative, and a false negative is a positive case missed by the classifier. All four entries must be counts from the same test set and decision threshold. Mixing percentages, training observations, or results from different thresholds makes the metrics invalid. Metric priorities depend on the application. In medical screening, missing a condition may be costly, so recall often receives more attention. In spam filtering or automated enforcement, false accusations may be costly, making precision important. Specificity is useful when correct rejection of negatives matters. Compare metrics across the same dataset, inspect class prevalence, and consider confidence intervals before declaring one model superior. The calculator provides deterministic point estimates for quick analysis, documentation, and threshold comparisons; it does not replace validation on representative data.