Confusion Matrices Trend 2026

A confusion matrix is a table used to evaluate classification model performance by comparing predicted versus actual outcomes. It displays true positives, false positives, true negatives, and false negatives. Data scientists and machine learning engineers use it to calculate accuracy, precision, recall, and F1-score. This helps identify model strengths and weaknesses, guiding improvements in fields like medical diagnostics, fraud detection, and customer analytics.