Overfitting is a common problem in artificial intelligence (AI) (as is underfitting) and machine learning, where a model learns the training data too well—including its noise, errors, and outliers—rather than just the underlying patterns. As a result, the model performs exceptionally on the training data but fails to generalize to new, unseen data, leading to poor predictive performance in real-world scenarios.
Overfitting typically occurs when:
• The model is too complex relative to the amount or diversity of training data (e.g., too many parameters for too little data).
• The model is trained for too long, allowing it to memorize specific details rather than learn general patterns.
• The training data contains a lot of noise or irrelevant information, which the model mistakenly treats as important.
• The dataset is too small or not representative of the full range of possible inputs.
Indicators of Overfitting
• High accuracy (or low error) on the training data, but much lower accuracy (or higher error) on validation or test data.
• The model makes poor predictions on new data, even though it performs well on the data it was trained on.
Real-World Example
Suppose you train a model to identify dogs in photos, but your training set mostly contains images of dogs in parks. The model might learn to associate grass with “dog” and fail to recognize a dog indoors, because it has overfit to the specific details of the training set.
Common strategies to avoid overfitting include:
• Using simpler models with fewer parameters.
• Increasing the size and diversity of the training dataset.
• Employing regularization techniques to penalize complexity.
• Using cross-validation to monitor performance on unseen data during training.