How does overfitting relate to parameters?

Can you explain how overfitting relates to the number of parameters in a model?

Sure! Overfitting occurs when a model learns the training data too well, including its noise and outliers. This often happens when the model has too many parameters relative to the amount of training data.

So, if I have a complex model with many parameters, how can I prevent overfitting?

You can prevent overfitting by using techniques like regularization, reducing the number of parameters, or using more training data.

What is regularization, and how does it help?

Regularization adds a penalty for larger coefficients in the model, which discourages complexity and helps generalize better to unseen data.

Are there specific types of regularization I should know about?

Yes, two common types are L1 regularization (Lasso) and L2 regularization (Ridge). L1 can lead to sparse models, while L2 tends to distribute weights more evenly.