Intuition behind Bias-Variance trade-off, Lasso and Ridge Regression

We can see that Ridge and Lasso is performing far better than Linear Regression when the correlation exists in the dataset. We can tune our penalty parameter further and try to find best value of RMSE. Here, 0.01 is the best value I got for lambda. So, we can use these regression methods when the variables are highly correlated. Hope this article was useful in understanding Bias-Variance trade-off, Lasso and Ridge Regression. Please feel free to comment, give feedback and share this article if you found it useful. You can find the original article on my blog here.