Loss functions

in mathematical optimization, a function (to be minimized) representing the cost of each outcome

average of the squares of the errors between estimated and actual values

In information theory, the cross-entropy between two probability distributions p and q, over the same underlying set of events, measures the average number of bits needed to identify an event drawn from the set when the coding scheme used for the set is optimized for an estimated probability distribution q, rather than the true distribution p.

loss function used in robust regression

in machine learning, a loss function used for maximum‐margin classification