A Regularization Toolbox
Explore various regularization strategies to control overfitting in machine learning models. Understand how adjusting network size, input variables, training duration, and learning rate help improve generalization. Learn to balance overfitting with underfitting and discover practical approaches such as early stopping and data augmentation to boost model performance.
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Combat overfitting through regularization
Just like tuning hyperparameters, reducing overfitting is more art than science. Besides L1 and L2, we can use many other regularization methods. An overview of some of the techniques is given below:
Small network size: The most fundamental regularization technique is to make the overfitting network smaller. It is also the most efficient technique. After all, overfitting happens because the system is too smart for the data it’s learning. Smaller networks are not as smart as big networks. We should try to reduce the number of hidden nodes or remove a few layers. We’ll use this approach in the chapter’s closing exercise.
Reduce input variables: Instead of simplifying the model, we can also ...