Overfitting and Underfitting

The core challenge in ML: finding the sweet spot between too simple and too complex.

The Bias-Variance Tradeoff

Detecting Overfitting

Preventing Overfitting

Key Takeaways

• Underfitting: Too simple, high bias, bad everywhere
• Overfitting: Too complex, high variance, memorizes training data
• Detection: Compare training vs test performance
• Prevention: Regularization, simpler models, more data, cross-validation