loss_Poisson_math: Laurae's Poisson Error (math function)

In Laurae2/Laurae: Advanced High Performance Data Science Toolbox for R

Description

This function computes the Laurae's Poisson Error loss per value provided x, y (preds, labels) counts.

Usage

loss_Poisson_math(x, y)

Arguments

x	The predictions.
y	The labels.

Details

This loss function is strictly positive, therefore defined in \]0, +Inf\[. It penalizes lower values more heavily, and as such is a good fit for typical problems requiring fine tuning when undercommitting on the predictions. The negative values are cancelled out to make the loss function positive, with loss = 0 when y_true = y_pred. This loss function is experimental.

Loss Formula : (y_pred - y_true * log(y_pred))

Gradient Formula : 1 - y_true/y_pred

Hessian Formula : y_true/(y_pred * y_pred)

Value

The Laurae's Poisson Error per value.

Examples

## Not run: 
SymbolicLoss(fc = loss_poisson_math, xmin = 1, xmax = 50, y = rep(10, 21))

## End(Not run)

Laurae2/Laurae documentation built on May 8, 2019, 7:59 p.m.