What is Overfitting in Supervised Learning?
Overfitting is a common problem in supervised learning, where a model learns the training data too well, capturing noise and outliers as if they were true patterns. This leads to a model that performs excellently on the training dataset but fails to generalize to unseen data, resulting in poor performance on the validation or test set.
Causes of Overfitting
- Complex Models: High-capacity models can memorize the training sample instead of learning general patterns.
- Insufficient Data: A limited amount of training data makes it easier for models to recognize noise as important information.
- Excessive Training: Too many training epochs allow the model to fit every detail of the training data.
Signs of Overfitting
To identify overfitting, one can analyze the model’s performance. A significant gap between training and validation accuracy or loss typically indicates overfitting.
Mitigation Strategies
- Regularization: Techniques like L1 and L2 regularization help limit the model complexity.
- Cross-Validation: Using techniques like k-fold cross-validation provides a better evaluation of model performance on unseen data.
- Dropout: In neural networks, dropout layers randomly deactivate neurons during training to prevent reliance on specific features.
By understanding and addressing overfitting, developers can build more robust supervised learning models that generalize well to new datasets.