What are the advantages and limitations of using Ridge regression over ordinary linear regression?
- Increases bias, Reduces variance, Reduces multicollinearity, Can cause overfitting
- Increases bias, Reduces variance, Tackles multicollinearity, Can cause underfitting
- Reduces overfitting, Increases variance, Lower bias, Lower variance
- Reduces overfitting, Tackles multicollinearity, Lower bias, Lower variance
Ridge regression helps in reducing overfitting by penalizing large coefficients through L2 regularization. It can handle multicollinearity but increases bias, potentially leading to underfitting. Ordinary linear regression lacks these regularization properties.
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