What is Ridge Regularization?
An example of an AI chatbot conversation about Ridge Regularization in machine learning.
Can you explain what Ridge Regularization is?
Ridge Regularization is a technique used in linear regression to prevent overfitting by adding a penalty to the size of coefficients.
How does it differ from Lasso Regularization?
While both are regularization techniques, Ridge adds a penalty based on the square of coefficients, whereas Lasso uses the absolute values.
When should I use Ridge Regularization?
You should use Ridge when you have multicollinearity in your data or when you want to keep all features in your model.
Can you give me an example of Ridge Regularization in practice?
Sure! In a dataset with many features, Ridge can help improve model performance by reducing the impact of less important features.
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