- 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 Â 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