TweedieLoss Class

Definition

Namespace:

Microsoft.ML.Trainers

Assembly:

Microsoft.ML.Data.dll

Package:

Microsoft.ML v3.0.1

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

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

type TweedieLoss = class
    interface IRegressionLoss
    interface IScalarLoss
    interface ILossFunction<single, single>

Public NotInheritable Class TweedieLoss
Implements ILossFunction(Of Single, Single), IRegressionLoss

Inheritance

Object

TweedieLoss

Implements

ILossFunction<Single,Single> IRegressionLoss IScalarLoss

Remarks

The Tweedie Loss function is defined as:

$ L(\hat{y}, y, i) = \begin{cases} \hat{y} - y ln(\hat{y}) + ln(\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{cases} $

where $\hat{y}$ is the predicted value, $y$ is the true label, $\Gamma$ 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)