Search results
Results from the WOW.Com Content Network
Given the binary nature of classification, a natural selection for a loss function (assuming equal cost for false positives and false negatives) would be the 0-1 loss function (0–1 indicator function), which takes the value of 0 if the predicted classification equals that of the true class or a 1 if the predicted classification does not match ...
As defined above, the Huber loss function is strongly convex in a uniform neighborhood of its minimum =; at the boundary of this uniform neighborhood, the Huber loss function has a differentiable extension to an affine function at points = and =. These properties allow it to combine much of the sensitivity of the mean-unbiased, minimum-variance ...
In many applications, objective functions, including loss functions as a particular case, are determined by the problem formulation. In other situations, the decision maker’s preference must be elicited and represented by a scalar-valued function (called also utility function) in a form suitable for optimization — the problem that Ragnar Frisch has highlighted in his Nobel Prize lecture. [4]
Main page; Contents; Current events; Random article; About Wikipedia; Contact us; Pages for logged out editors learn more
In some sense the 0-1 indicator function is the most natural loss function for classification. It takes the value 0 if the predicted output is the same as the actual output, and it takes the value 1 if the predicted output is different from the actual output.
The use of the MAPE as a loss function for regression analysis is feasible both on a practical point of view and on a theoretical one, since the existence of an optimal model and the consistency of the empirical risk minimization can be proved. [1]
This is also known as the log loss (or logarithmic loss [4] or logistic loss); [5] the terms "log loss" and "cross-entropy loss" are used interchangeably. [ 6 ] More specifically, consider a binary regression model which can be used to classify observations into two possible classes (often simply labelled 0 {\displaystyle 0} and 1 ...
The loss function is a function that maps values of one or more variables onto a real number intuitively representing some "cost" associated with those values. For backpropagation, the loss function calculates the difference between the network output and its expected output, after a training example has propagated through the network.