TweedieLoss Class
Definition
Important
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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.IRegressionLosstype TweedieLoss = class
interface IRegressionLoss
interface IScalarLoss
interface ILossFunction<single, single>Public NotInheritable Class TweedieLoss
Implements ILossFunction(Of Single, Single), IRegressionLoss- Inheritance
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TweedieLoss
- Implements
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) |