loss_Poisson_xgb: Laurae's Poisson Error (xgboost function)
In Laurae2/Laurae: Advanced High Performance Data Science Toolbox for R

Description Usage Arguments Details Value

This function computes for xgboost's obj function the Laurae's Poisson Error loss gradient and hessian per value provided preds and dtrain. Negative and null values are set to 1e-15.

1	loss_Poisson_xgb(preds, dtrain)

`preds`	The `predictions`.
`dtrain`	The xgboost model.

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)

The gradient and the hessian of the Laurae's Poisson Error per value in a list.

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