Let's now consider a parameterized model with a single vectorial parameter (this isn't a limitation, but only a didactic choice):
The goal of a learning process is to estimate the parameter θ so as, for example, to maximize the accuracy of a classification. We define the bias of an estimator (in relation to a parameter θ):
In other words, the bias is the difference between the expected value of the estimation and the real parameter value. Remember that the estimation is a function of X, and cannot be considered a constant ...
