midTensorFlow
What causes overfitting in TensorFlow models and how is it detected?
Updated May 16, 2026
Short answer
Overfitting occurs when a model learns training data too well but fails to generalize.
Deep explanation
Overfitting happens when model complexity exceeds data variability. The model memorizes noise instead of patterns. It is detected when training accuracy is high but validation accuracy is low.
Real-world example
Image classifier performs well on training but fails in real-world images.
Common mistakes
- Using too many layers without regularization.
Follow-up questions
- How does dropout help?
- What is early stopping?