...
During training we optimize the loss function, i.e. reduce the error between actual and predicted values. Since we deal with a binary classification problem, the can take on just two values,
(for hypothesis
) and
= 1 (for hypothesis
).
A popular algorithm to optimize the weights consists of iteratively modifying the weights after each training observation or after a bunch of training observations by doing a minimization of the loss function.
...