Evaluative Metrics

Evaluative metrics are tools used to measure the performance of a model or system, often in tasks like classification or prediction. They help determine how well the system is working by providing quantitative scores. Common metrics include accuracy (how often the model is right), precision (how often positive predictions are correct), recall (how many actual positives are identified), and F1 score (a balance of precision and recall). These metrics help compare different models and guide improvements, ensuring that the system meets desired standards and functions effectively in real-world applications.