By: Bob Vu

Bob Vu — Thu, 20 Jun 2019 00:41:21 +0000

1. Under-fit (high bias): More training data doesn’t help, so don’t waste time on collecting more data.
2. Over-fit (high variance): getting more training data is likely to help.
Choosing reasonable number of features, degree of polynomial, and appropriate regularization parameter (lambda) is the key to keep balance between Overfit and Underfit.
Training set (60%), Cross Verification Set (20%), Test Set (20%) is helpful in choosing the best polynomial degree and regularization parameter

Comments on: Bias vs Variance Quick note

By: Bob Vu