Machine Learning Model Evaluation Guide

CONTEXT
In the field of machine learning, evaluating a model's performance is crucial to ensure it meets the required standards and objectives. Understanding various evaluation metrics and techniques can help improve model accuracy and reliability.

OBJECTIVE
This prompt aims to provide a comprehensive overview of model evaluation strategies, including the selection of appropriate metrics based on the type of problem (classification or regression) and the implications of these metrics on model performance.

FORMAT
The response should include a detailed explanation of different evaluation metrics, examples of scenarios where each metric is applicable, and tips for interpreting the results accurately.

EXAMPLES
If you have a binary classification problem, you might focus on accuracy, precision, recall, and F1-score. For regression tasks, you may consider metrics like Mean Absolute Error (MAE), Mean Squared Error (MSE), and R-squared. Additionally, include a discussion on the importance of cross-validation and the concept of bias-variance tradeoff.