Data Science, Machine Learning, Linear Regression, Ridge Regression, Lasso Regression.
where: yi is the ith value of the variable we want to predict. β0 is the y-intercept. βj is the coefficient for the jth predictor variable xij. λ is the regularization parameter. This penalty term discourages large coefficients, like in Lasso, but because of the squaring, it does not force them to