2- Model Selection and Evaluation Criteria

a. Model Selection:

• Choose appropriate models based on the nature of the problem.
• Consider factors like interpretability, complexity, and model assumptions.

b. Evaluation Criteria:

• Use metrics like accuracy, precision, recall, F1 score, and ROC-AUC for classification models.
• For regression, assess metrics such as Mean Squared Error (MSE) or Root Mean Squared Error (RMSE).