What are the differences between OLS and other regression methods?

What are the differences between OLS and other regression methods?

OLS, or Ordinary Least Squares, is a method for estimating the parameters in a linear regression model. It minimizes the sum of the squares of the residuals. Other methods, like Ridge and Lasso regression, add penalties to the loss function to prevent overfitting.

Can you explain Ridge and Lasso regression?

Sure! Ridge regression adds an L2 penalty to the loss function, which helps to shrink the coefficients. Lasso regression, on the other hand, adds an L1 penalty, which can actually reduce some coefficients to zero, effectively performing variable selection.

When should I use Ridge or Lasso instead of OLS?

You should consider Ridge or Lasso when you have multicollinearity in your data or when you want to prevent overfitting, especially with high-dimensional datasets.

Are there any other regression methods I should know about?

Yes, there are several other methods like Elastic Net, Polynomial Regression, and Support Vector Regression, each with its own advantages depending on the data and problem.