Model Performance Metrics

Model performance metrics are tools used to evaluate how well a predictive model works. Metrics like accuracy show the percentage of correct guesses, while precision and recall assess how well the model identifies positive cases without false alarms. The F1 score balances precision and recall into a single number, and the ROC-AUC measures how well the model distinguishes between classes across different thresholds. These metrics help determine if a model is reliable and suitable for decision-making, ensuring it performs effectively in real-world situations.