ILossT Interface |
Namespace: Accord.Math.Optimization.Losses
The ILossT type exposes the following members.
Name | Description | |
---|---|---|
Loss |
Computes the loss between the expected values (ground truth)
and the given actual values that have been predicted.
(Inherited from ILossTScore, TLoss.) |
In mathematical optimization, statistics, decision theory and machine learning, a loss function or cost function is a function that maps an event or values of one or more variables onto a real number intuitively representing some "cost" associated with the event. An optimization problem seeks to minimize a loss function. An objective function is either a loss function or its negative (sometimes called a reward function, a profit function, a utility function, a fitness function, etc.), in which case it is to be maximized.
References: