Poisson¶
Poisson loss.
The Poisson loss is usually more suited for regression with count data than the squared loss.
Mathematically, it is defined as
\[L = exp(p_i) - y_i \times p_i\]
Its gradient w.r.t. to \(p_i\) is
\[\frac{\partial L}{\partial p_i} = exp(p_i) - y_i\]
Methods¶
call
Returns the loss.
Parameters
- y_true
- y_pred
Returns
The loss(es).
gradient
Return the gradient with respect to y_pred.
Parameters
- y_true
- y_pred
Returns
The gradient(s).
mean_func
Mean function.
This is the inverse of the link function. Typically, a loss function takes as input the raw output of a model. In the case of classification, the raw output would be logits. The mean function can be used to convert the raw output into a value that makes sense to the user, such as a probability.
Parameters
- y_pred
Returns
The adjusted prediction(s).