Small Alpha ==> High-grade polynomials ==> More Complex H ==> better chance of approximation F ==> Overfitting ==> Only good for training data not for estimation
Big Alpha ==> Less complex H ==> better chance of generalization out of F ==> Underfitting ==> No proper answer for training data
The correct Alpha ==> Where there is a correct balance between approximation and generalization.
The result for attached code is as below:
As we see by increasing the alpha we have smoother line which is less fit on the train data.
