Model Evaluation and Metrics

In this lesson, we will learn model evaluation and metrics. Train/test performance, confusion matrix, accuracy, precision, recall, F1-score, ROC-AUC, MAE, RMSE, R², and the impact of metric selection on business decisions will be explained. ## Lesson objective In this lesson, we will learn Model Evaluation and Metrics. In the previous lesson, we covered Classification Models. We explained binary classification, multi-class classification, probability output, threshold, Logistic Regression, Decision Tree, Random Forest, confusion matrix, True Positive, False Positive, True Negative, and False Negative.