LogLoss Class
Definition
The Log Loss, also known as the Cross Entropy Loss. It is commonly used in classification tasks.
public sealed class LogLoss : Microsoft.ML.Trainers.ILossFunction<float,float>, Microsoft.ML.Trainers.ISupportSdcaClassificationLoss
type LogLoss = class
interface ISupportSdcaClassificationLoss
interface ISupportSdcaLoss
interface IScalarLoss
interface ILossFunction<single, single>
interface IClassificationLoss
Public NotInheritable Class LogLoss
Implements ILossFunction(Of Single, Single), ISupportSdcaClassificationLoss
 Inheritance

LogLoss
 Implements
Remarks
The Log Loss function is defined as:
$L(p(\hat{y}), y) = y ln(\hat{y})  (1  y) ln(1  \hat{y})$
where $\hat{y}$ is the predicted score, $p(\hat{y})$ is the probability of belonging to the positive class by applying a sigmoid function to the score, and $y \in \{0, 1\}$ is the true label.
Note that the labels used in this calculation are 0 and 1, unlike Hinge Loss and Exponential Loss, where the labels used are 1 and 1.
The Log Loss function provides a measure of how certain a classifier's predictions are, instead of just measuring how correct they are. For example, a predicted probability of 0.80 for a true label of 1 gets penalized more than a predicted probability of 0.99.
Constructors
LogLoss() 
Methods
ComputeDualUpdateInvariant(Single)  
Derivative(Single, Single)  
DualLoss(Single, Single)  
DualUpdate(Single, Single, Single, Single, Int32)  
Loss(Single, Single) 
Applies to
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