Accuracy is the simplest way to measure a model's performance: the percentage of predictions it gets right out of all predictions made. If a model reviewing 1000 loan applications classifies 930 correctly, its accuracy is 93 percent. Beware, though: accuracy can mislead on imbalanced data. If only 2 percent of transactions at a Moroccan bank are fraudulent, a lazy model that flags nothing is 98 percent accurate yet completely useless, which is why metrics like F1-score exist.