What is overfitting in Gradient Descent-based models?
Updated May 16, 2026
Short answer
Overfitting occurs when a model learns training data too well but fails to generalize.
Deep explanation
During Gradient Descent training, excessive optimization on training loss can lead to memorization instead of generalization. Regularization techniques are used to prevent this.
Real-world example
A model predicting house prices perfectly on training data but poorly on new data.
Common mistakes
- Training too long without validation monitoring.
Follow-up questions
- How to prevent overfitting?
- What is early stopping?