Welcome to Our Model Evaluation Guide

Introduction to Model Evaluation

Evaluating machine learning models is essential to ensure that they perform well both on training data and future unseen data. In this guide, we'll walk you through key concepts and metrics used for model evaluation.

Key Concepts

• Accuracy: The ratio of correctly predicted instances to the total instances.
• Precision and Recall: Metrics that consider the balance between positive class predictions and actual occurrences.
• F1 Score: The harmonic mean of precision and recall, providing a balance between the two.

Model Evaluation Techniques

Learn about various techniques such as cross-validation, bootstrapping, and more.

Further Reading

Explore more with detailed articles:

• Understanding Precision and Recall
• Improving Model Accuracy