Machine Learning Model Evaluation Framework

CONTEXT
This prompt provides guidance on how to evaluate machine learning models based on various performance metrics. It aims to enhance understanding of different evaluation strategies.

OBJECTIVE
The objective is to assist practitioners in selecting and applying the most relevant evaluation metrics to assess the performance of their machine learning models accurately.

FORMAT
The response should include descriptions of common evaluation metrics (such as accuracy, precision, recall, F1 score, ROC-AUC, etc.), methods for model validation (such as cross-validation, train/test splits), and examples of how to implement these metrics using popular ML libraries (like sklearn).

EXAMPLES
1. 'For a classification model, to evaluate accuracy, use the following code snippet: from sklearn.metrics import accuracy_score ...' 2. 'To calculate the ROC-AUC score, you can implement: from sklearn.metrics import roc_auc_score ...'