Overfitting in machine learning occurs when a model learns not only the underlying patterns in the training data but also the noise and random fluctuations. This excessive learning leads to high accuracy on the training set but poor performance on new, unseen data, as the model fails to generalize effectively.
Several factors contribute to overfitting:
- Insufficient Training Data: A small dataset may not represent the full variability of the problem space, causing the model to memorize specific details.
- Model Complexity: Highly complex models with many parameters can fit the training data too closely, capturing noise as if it were a valid pattern.
- Noisy Data: Training data containing irrelevant or random information can mislead the model into learning incorrect associations.