Accuracy Score
Accuracy Score is a metric used in machine learning and statistics to evaluate the performance of classification models by measuring the proportion of correct predictions (both true positives and true negatives) out of the total number of predictions. It is calculated as (True Positives + True Negatives) / Total Predictions, providing a simple, intuitive measure of overall model correctness. However, it can be misleading in imbalanced datasets where one class dominates, as it may not reflect performance on minority classes.
Developers should learn and use Accuracy Score when building and evaluating classification models, especially in balanced datasets where all classes are equally important, such as in spam detection or medical diagnosis with similar prevalence. It is a fundamental metric for initial model assessment, but should be complemented with other metrics like precision, recall, or F1-score in imbalanced scenarios, such as fraud detection or rare disease prediction, to avoid skewed evaluations.