How is accuracy defined in the context of a classification model?

Accuracy in the context of a classification model is defined as the percentage of all correct predictions made by the model, which includes both true positives (correctly predicted positive instances) and true negatives (correctly predicted negative instances). This metric provides a general measure of the model's performance across all classes by quantifying how often the model is correct, regardless of the classification label.

Additionally, considering the context of classification, accuracy is particularly important as it offers insight into the overall effectiveness of the model in correctly identifying both the positive and negative instances in the dataset. This metric is critical for evaluating model performance when classes are balanced; however, it may not be the best measure for imbalanced datasets where one class is significantly more prevalent than the other.