[Machine Learning] Diagnosing Bias vs. Variance

In this section we examine the relationship between the degree of the polynomial d and the underfitting or overfitting of our hypothesis.

• We need to distinguish whether bias or variance is the problem contributing to bad predictions.
• High bias is underfitting and high variance is overfitting. Ideally, we need to find a golden mean between these two.

The training error will tend to decrease as we increase the degree d of the polynomial.

At the same time, the cross validation error will tend to decrease as we increase d up to a point, and then it will increase as d is increased, forming a convex curve.

[Machine Learning] Diagnosing Bias vs. Variance

原文：https://www.cnblogs.com/Answer1215/p/13712627.html