Ridge Regression, Lasso Regression, Elasticnet Regression, Machine Learning, Machinelearningalgorithms.
(ˆy^i - y^i)^2] + λ (slope)² Now let’s observe how this will reduce the overfitting. hθ(x)=θ0+θ1x // θ0 is zero now θ0 = 0 hθ(x)=θ1x // Here θ1 is called as slope //I don't want to make J(θ0,θ1) to zero because of the overfitting condition. J(θ0,θ1) = 1/2m [i =1 -> m Σ (ˆy^i - y^i)^2] + λ (slope)²