Introduction to Ridge Regression

What is Ridge Regression?
Ridge regression is a model tuning method that is used to analyse any data that suffers from multicollinearity. This method performs L2 regularization. When the issue of multicollinearity occurs, least-squares are unbiased, and variances are large, this results in predicted values being far away from the actual values.

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What is ridge regression formula?,
What is ridge regression formula?,
When can we use ridge regression?,
When can we use ridge regression?,
Why is it called ridge regression?,
Why is it called ridge regression?,
,What is the difference between Ridge and linear regression?
, What is the difference between Ridge and linear regression?
,What is Alpha in ridge regression?
,What is Alpha in ridge regression?
,Is ridge regression linear regression?
,Is ridge regression linear regression?
,What are the types of regression?
,What is K in ridge regression?
,What does Ridge stand for?
,What is the benefit of ridge regression?
,How does ridge regression reduce Multicollinearity?
,What is lambda in ridge regression?
,What is Ridge CV?
,How does ridge regression work Python?
,What is elastic net regression?
,How does ridge regression reduce variance?
,What is Alpha in elastic net?
,How does ridge regression reduce Overfitting?
,What is lasso used for?
,Is ridge regression always unique?
,What is L1 and L2 regularization?,
What type of penalty is used on regression weights in ridge regression?
,What makes a good regression model?
,What is Ridge model?
,What is r2 in linear regression?

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