Overfitting

Executive Definition: Overfitting — When an AI model learns its training data too closely, memorizing noise and specific examples rather than general patterns, resulting in poor generalization to new data.

An overfitted model essentially memorizes the training examples. While it performs extremely well on the training dataset, it fails when exposed to unseen data. It is often identified by a large disparity between training accuracy and validation accuracy. Common preventative measures include adding regularization, reducing model complexity, or expanding the training dataset.

Frequently Asked Questions

How do I detect overfitting?

Compare the model’s performance on the training data with its performance on a separate validation set. High training accuracy coupled with low validation accuracy is a primary indicator.

What causes overfitting?

Common causes include training on insufficient data, using an overly complex model, or training for too many epochs on noisy data.

How do I prevent overfitting?

Techniques include increasing the size of the training dataset, applying regularization (like L1/L2 penalties or dropout), early stopping, and data augmentation.