ExpLoss Class

Definition

Namespace:

Microsoft.ML.Trainers

Assembly:

Microsoft.ML.Data.dll

Package:

Microsoft.ML v5.0.0-preview.1.25125.4

Source:

LossFunctions.cs

Exponential Loss, commonly used in classification tasks.

C#

public sealed class ExpLoss : Microsoft.ML.Trainers.IClassificationLoss, Microsoft.ML.Trainers.ILossFunction<float,float>

Inheritance

Object

ExpLoss

Implements

IClassificationLoss ILossFunction<Single,Single> IScalarLoss

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)

Applies to