What is Overfitting in Deep Learning?
Overfitting is a common problem in deep learning, where a model learns the training data too well, capturing noise and details to the extent that it negatively impacts its performance on new, unseen data. Essentially, the model becomes overly complex, tailoring itself too closely to the training set rather than generalizing from it.
Key Characteristics of Overfitting:
- High Training Accuracy: The model shows excellent performance on the training dataset.
- Low Validation/Test Accuracy: When applied to validation or test datasets, the model performs poorly.
- Complex Models: Overly complex models with too many parameters are more prone to overfitting.
Causes of Overfitting:
- Insufficient training data.
- Noise in the data.
- Too many features relative to the number of observations.
How to Prevent Overfitting:
- Regularization: Techniques like L1 and L2 regularization help constrain the model.
- Dropout: Randomly dropping units during training helps promote generalization.
- Early Stopping: Halting training when performance on validation data starts to decline.
- Data Augmentation: Increasing the variety of training examples helps in making the model robust.