Model error estimation

We said that the trained model is used to predict the labels of new data, and the quality of the prediction depends on the ability of the model to generalize, that is, the correct prediction of cases not present in the trained data. This is a well-known problem in literature and related to two concepts: bias and variance of the outputs.

The bias is the error due to a wrong assumption in the algorithm. Given a point x^(t) with label y_t, the model is biased if it is trained with different training sets, and the predicted label y_t^pred will always be different from y_t. The variance error instead refers to the different, wrongly predicted labels of the given point x^(t). A classic example to explain the concepts is to consider a circle with the true value at the center (true label), as shown in the following figure. The closer the predicted labels are to the center, the more unbiased the model and the lower the variance (top left in the following figure). The other three cases...