TweedieLoss Class

Definition

Namespace:

Microsoft.ML.Trainers

Assembly:

Microsoft.ML.Data.dll

Package:

Microsoft.ML v4.0.0-preview.24522.7

Source:

LossFunctions.cs

Tweedie loss, based on the log-likelihood of the Tweedie distribution. This loss function is used in Tweedie regression.

C#

public sealed class TweedieLoss : Microsoft.ML.Trainers.ILossFunction<float,float>, Microsoft.ML.Trainers.IRegressionLoss

Inheritance

Object

TweedieLoss

Implements

ILossFunction<Single,Single> IRegressionLoss IScalarLoss

Remarks

The Tweedie Loss function is defined as:

$L(\hat{y},y,i)=\{\begin{array}{ll}\hat{y}-yln(\hat{y})+ln(\mathrm{\Gamma }(y))& \text{if }i=1\\ \\ \hat{y}+\frac{y}{\hat{y}}-\sqrt{y}& \text{if }i=2\\ \\ \frac{(\hat{y}{)}^{2-i}}{2-i}-y\frac{(\hat{y}{)}^{1-i}}{1-i}-(\frac{y^{2-i}}{2-i}-y\frac{y^{1-i}}{1-i})& \text{otherwise}\end{array}$

where $\hat{y}$ is the predicted value, $y$ is the true label, $\mathrm{\Gamma }$ is the Gamma function, and $i$ is the index parameter for the Tweedie distribution, in the range [1, 2]. $i$ is set to 1.5 by default. $i=1$ is Poisson loss, $i=2$ is gamma loss, and intermediate values are compound Poisson-Gamma loss.

Constructors

TweedieLoss(Double)

Constructor for Tweedie loss.

Methods

Derivative(Single, Single)

Loss(Single, Single)

Applies to