What is the difference between regularization and other techniques in Python?

What is the difference between regularization and other techniques in Python?

Regularization is a technique used to prevent overfitting in machine learning models by adding a penalty to the loss function. Other techniques, like feature selection or data augmentation, focus on improving model performance through different means.

Can you explain how L1 and L2 regularization work?

Sure! L1 regularization adds a penalty equal to the absolute value of the coefficients, promoting sparsity. L2 regularization adds a penalty equal to the square of the coefficients, which helps in reducing the magnitude of coefficients but does not promote sparsity.

When should I use regularization instead of just increasing my dataset size?

Regularization is beneficial when you have a small dataset or when your model is complex and prone to overfitting. Increasing dataset size can help, but if the model complexity is too high, regularization can provide a more immediate solution.

Are there any scenarios where regularization might not be helpful?

Yes, if your dataset is large and diverse enough, regularization might not be necessary. In such cases, it could even hinder the model's ability to learn complex patterns.