accuracy measures

Accuracy measures how well a model's predictions match actual outcomes. It is calculated by dividing the number of correct predictions by the total number of predictions made. For example, if a model predicts correctly 90 times out of 100, its accuracy is 90%. While a useful indicator, accuracy alone might not reflect the model's effectiveness in specific situations, especially if the data has imbalanced categories. In such cases, additional metrics like precision, recall, or F1 score can provide more nuanced insights into performance.