SmoothedHingeLoss Class
Definition
A smooth version of the HingeLoss function, commonly used in classification tasks.
public sealed class SmoothedHingeLoss : Microsoft.ML.Trainers.ILossFunction<float,float>, Microsoft.ML.Trainers.ISupportSdcaClassificationLoss
type SmoothedHingeLoss = class
interface ISupportSdcaClassificationLoss
interface ISupportSdcaLoss
interface IScalarLoss
interface ILossFunction<single, single>
interface IClassificationLoss
Public NotInheritable Class SmoothedHingeLoss
Implements ILossFunction(Of Single, Single), ISupportSdcaClassificationLoss
 Inheritance

SmoothedHingeLoss
 Implements
Remarks
Let $f(\hat{y}, y) = 1  y\hat{y}$, where $\hat{y}$ is the predicted score and $y \in \{1, 1\}$ is the true label. $f(\hat{y}, y)$ here is the nonzero portion of the Hinge Loss.
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 Smoothed Hinge Loss function is then defined as:
$ L(f(\hat{y}, y)) = \begin{cases} 0 & \text{if } f(\hat{y}, y) < 0 \\ \frac{(f(\hat{y}, y))^2}{2\alpha} & \text{if } f(\hat{y}, y) < \alpha \\ f(\hat{y}, y)  \frac{\alpha}{2} & \text{otherwise} \end{cases} $
where $\alpha$ is a smoothing parameter set to 1 by default.
Constructors
SmoothedHingeLoss(Single) 
Constructor for smoothed hinge losee. 
Methods
ComputeDualUpdateInvariant(Single)  
Derivative(Single, Single)  
DualLoss(Single, Single)  
DualUpdate(Single, Single, Single, Single, Int32)  
Loss(Single, Single) 
Applies to
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