Overfitting/underfitting
We can see that linear function is not sufficient to fit the training samples. This is called underfitting.
A polynomial of degree 2 approximates the true function almost perfectly.
However, for higher degrees of polynomial, the model will overfit the training data.
We evaluate quantitively overfitting/underfitting by using cross-validation. we calculate the error on the test set and compare it with error on training set to determine overfitting or underfitting.
High Bias versus High Variance
High Bias - Both training and test errors are high and both errors are more or less the same.
High Variance - Training error is low but testing is very high compared to training error.
