ExpLoss Class
Definition
Important
Some information relates to prerelease product that may be substantially modified before it’s released. Microsoft makes no warranties, express or implied, with respect to the information provided here.
Exponential Loss, commonly used in classification tasks.
public sealed class ExpLoss : Microsoft.ML.Trainers.IClassificationLoss, Microsoft.ML.Trainers.ILossFunction<float,float>type ExpLoss = class
interface IClassificationLoss
interface IScalarLoss
interface ILossFunction<single, single>Public NotInheritable Class ExpLoss
Implements IClassificationLoss, ILossFunction(Of Single, Single)- Inheritance
-
ExpLoss
- Implements
Remarks
The Exponential Loss function is defined as:
$L(\hat{y}, y) = e^{-\beta y \hat{y}}$
where $\hat{y}$ is the predicted score, $y \in \{-1, 1\}$ is the true label, and $\beta$ is a scale factor set to 1 by default.
Note that the labels used in this calculation are -1 and 1, unlike Log Loss, where the labels used are 0 and 1. Also unlike Log Loss, $\hat{y}$ is the raw predicted score, not the predicted probability (which is calculated by applying a sigmoid function to the predicted score).
The Exponential Loss function penalizes incorrect predictions more than the Hinge Loss and has a larger gradient.
Constructors
| ExpLoss(Single) |
Methods
| Derivative(Single, Single) | |
| Loss(Single, Single) |