What are common mistakes in regularization?

What are some common mistakes people make when applying regularization in machine learning?

Some common mistakes include using too much regularization, not tuning hyperparameters properly, and misunderstanding the data.

Can you explain how too much regularization can affect my model?

Certainly! Too much regularization can lead to underfitting, where the model is too simple to capture the underlying patterns in the data.

What about not tuning hyperparameters? Why is that important?

Not tuning hyperparameters can result in suboptimal model performance. Each dataset may require different regularization strengths to perform well.

How can I avoid these mistakes?

You can avoid these mistakes by conducting thorough experiments, using cross-validation, and carefully analyzing your model's performance.