Training
Overfitting
When a model learns the training data too specifically and performs poorly on new, unseen data.
Overfitting happens when a model memorizes patterns, noise, or quirks in the training set instead of learning generalizable structure. The result is strong performance on training data but weak performance in the real world.
This is a common risk when models are too large for the dataset, trained too long, or fine-tuned on narrow data without enough variation. It is especially relevant in smaller enterprise datasets and custom model adaptation projects.
Classic warning sign: training performance improves, but validation performance stalls or declines. The model is getting better at memorizing, not generalizing.
Ways to Reduce Overfitting
- Use more data β especially diverse, representative examples
- Regularize β techniques like dropout or weight decay
- Stop earlier β use early stopping based on validation metrics
- Simplify the model β reduce capacity where appropriate
Overfitting is the opposite of underfitting. Good model development aims for the balance where the system learns meaningful patterns without becoming overly attached to the training set.