Machine Learning Model Evaluation Guide

CONTEXT
In the field of machine learning, evaluating models is crucial to understanding their performance and ensuring reliability. This prompt aims to outline best practices for model evaluation.

OBJECTIVE
To provide a structured approach for evaluating machine learning models, including metrics selection and validation methods.

FORMAT
A step-by-step guide that explains key evaluation metrics and strategies for comparative analysis of machine learning models.

EXAMPLES
1. Use of confusion matrix for classification model performance. 2. Application of cross-validation techniques to avoid overfitting.