Accuracy is a metric used in machine learning to evaluate the performance of a model. It measures the percentage of correct predictions made by the model in relation to the total number of predictions made. In classification tasks, accuracy is the fraction of predictions the model got right. It is calculated as follows:
Accuracy = Number of correct predictions / Total number of predictions
Accuracy is a useful metric when all classes are of equal importance. However, it can be deceptive when working with class-imbalanced datasets, where there is a significant disparity between the number of positive and negative examples. In such cases, a model that always predicts the majority class can achieve high accuracy, even though it has no predictive ability to distinguish between the classes.
To get a more complete picture of a models performance, other metrics such as precision and recall should also be considered. Precision measures how often the model correctly predicts the positive class, while recall measures how often the model correctly identifies positive examples. These metrics are particularly useful when working with imbalanced datasets.
