Diagnosing Bias and Variance using the learning curve

By Mohammed A. Shehab

Overview

This tutorial uses learning curves to diagnose bias and variance in machine learning models. It provides a comprehensive understanding of these key concepts and their impact on model performance.

Objective

The primary objective of this project is to demonstrate how to analyze learning curves for training and cross-validation datasets to diagnose bias and variance issues in machine learning models.

Key Concepts

• High Bias: A model exhibits high bias if the gap between the training and cross-validation curves is significant, indicating underfitting.
• High Variance: A model exhibits high variance if the gap between the training and cross-validation curves is small, even with increased data size, indicating overfitting.
• Optimal Model Complexity: An optimal model complexity is achieved when the training and cross-validation curves intersect, indicating a well-balanced model.

Tutorial:

Jupyter Notebook.

Conclusion

This tutorial provides valuable insights into diagnosing bias and variance in machine learning models using learning curves. Practitioners can enhance model performance and build more robust machine-learning systems by understanding and addressing these issues.