Learning metrics

Learning metrics are tools used to measure how well a machine learning model performs. They evaluate how accurately the model makes predictions or classifications on data it hasn't seen before. Common metrics include accuracy (how often the model is correct), precision and recall (how well it identifies true positives without false alarms), and F1 score (a balance between precision and recall). These metrics help developers understand the strengths and weaknesses of a model, guiding improvements and ensuring it meets the desired performance standards for real-world applications.