Diagnosing bias vs. variance

Most pitfall in machine learning is (bias)underfitting vs (variance)overfitting problems
Knowing which happening is the important way to understand more, and fix our learning algorithms

These graph will understand bias/variance better

more and more to the right of the diagram is the higher order polynomials (more complicated)
Typically more high order polynomials less the error it will be
Cross validation error should be approach the test error
d = 2 is the better one because less will be underfitting and d = 4 will be overfitting

Learning algorightm is far from correct denotes by high point in the graph above
But which are the bias/variance?
left square red is the example of high bias
right square red is the example of high variance
To avoid underfitting or the overfitting, we must go into inside the barrier that shown from each high bias boundaries and high variance boundaries respectively

An example of high variance (overfitting) where the Jtrain(error) is very low = 0.10 , But the cross validation value much more higher = 0.3
Occurred because the parameters much more fit to train set rathet than cross validation set.

Later, learning algorithm in more detail from suffering of high variance/ high bias