In this video, we have covered what is Lasso & ridge regression & what are the problems that one can encounter when using these techniques to train the models.

Lasso and ridge regression is a regularization technique, and it comes to training models. There are two problems one can encounter - Overfitting & Underfitting.

Let us see in detail what is overfitting & underfitting.

1) Overfitting - A statistical model or a machine learning algorithm is said to have under-fitting when it cannot capture the underlying trend of the data.
2) Underfitting - A statistical model is said to be over-fitted when we train it with a lot of data (just like fitting ourselves in oversized pants!). When a model gets trained with so much data, it starts learning from the noise and inaccurate data entries in our data set.

Ridge Regression - Ridge regression is a classification algorithm that works in part as it doesn’t require unbiased estimators.

Lasso Regression - Lasso regression is a regularization technique. It is used over regression methods for a more accurate prediction

Related Article :  https://www.geeksforgeeks.org/implementation-of-lasso-ridge-and-elastic-net/