Methods to prevent overfitting in Machine Laerning
L2 Regularization (Ridge Regression)
L2 regularization adds a penalty term to the loss function based on the squared magnitudes of the model’s weights. This penalty discourages large weight values and encourages the model to use smaller weights, leading to a smoother and more generalized solution. The regularization term is controlled by a hyperparameter (lambda or alpha) that balances the trade-off between fitting the training data and keeping the weights small.
L1 Regularization (Lasso Regression)
L1 regularization adds a penalty term to the loss function based on the absolute magnitudes of the model’s weights. Similar to L2 regularization, L1 regularization also encourages the model to use smaller weights, but it has the additional property of driving some weights to exactly zero. This makes L1 regularization useful for feature selection and creating sparse models.
Other methods to prevent overfitting include:
Dropout is a technique used primarily in neural networks. During training, random neurons are temporarily dropped out (set to zero) with a given probability. This forces the network to learn more robust features and prevents it from relying too heavily on specific neurons, thereby reducing overfitting.
Cross-validation is a technique used to assess the model’s performance on different subsets of the data. It helps in evaluating how well the model generalizes to new data and can provide insights into overfitting issues.
Early stopping involves monitoring the model’s performance on a validation set during training. Training is stopped when the model’s performance on the validation set starts to degrade, preventing it from overfitting the training data.
Data augmentation involves generating additional training data by applying random transformations (e.g., rotations, flips, shifts) to the original data. This helps in increasing the size and diversity of the training data, reducing the risk of overfitting.