Question

You train a ridge regression model, you get a R^2 of 1 on your training data and you get a R^2 of 0 on your validation data; what should you do?
1 point
Your model is overfitting, so increase the parameter alpha
Your model is under fitting; so perform a polynomial transform
Nothing, your model performs flawlessly on your validation data

Answer 1

Answer:

You should see that the optimal value of alpha is 20, with a negative MSE of -3.07267. This is a slight improvement upon the basic multiple linear regression.

Explanation:

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Answer 2

Answer:

A regression model that uses L1 regularization technique is called Lasso Regression and model which uses L2 is called Ridge Regression.

Explanation:

From the above question,

They have given :

         A regression model that uses L1 regularization technique is called Lasso Regression and model which uses L2 is called Ridge Regression. The key difference between these two is the penalty term. Ridge regression adds “squared magnitude” of coefficient as penalty term to the loss function.

       Your model is overfitting, so increase the parameter alpha. A $R^{2}$ of 1 on the training data indicates that the model is fitting the training data perfectly, but a $R^{2}$ of 0 on the validation data suggests that the model is not generalizing well to new data.

   Increasing the regularization parameter alpha in ridge regression helps to reduce overfitting and improve the model's ability to generalize to new data.

A regression model that uses L1 regularization technique is called Lasso Regression and model which uses L2 is called Ridge Regression.

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