Accuracy

Accuracy is a metric used to evaluate the performance of machine learning models and algorithms. It measures the proportion of true results (both true positives and true negatives) among the total number of cases examined. High accuracy indicates that the model correctly predicts a large number of instances, making it a popular choice for assessing classification models. However, accuracy alone can be misleading, especially in cases of imbalanced datasets where one class significantly outnumbers another. Thus, it is often used in conjunction with other metrics such as precision, recall, and F1 score to provide a more comprehensive evaluation of model performance.