Poisson Class

Some of the trainers accept a loss parameter that will be used for training. It is also known as loss function, objective function, or optimization score function.

Inheritance

builtins.object

Poisson

Constructor

Poisson()

Examples

   ###############################################################################
   # Poisson Loss
   from nimbusml.linear_model import OnlineGradientDescentRegressor
   from nimbusml.loss import Poisson

   # specifying the loss function as a string keyword
   trainer1 = OnlineGradientDescentRegressor(loss='poisson')

   # can also use loss class instead of string

   trainer2 = OnlineGradientDescentRegressor(
       loss=Poisson())  # equivalent to loss='tweedie'

Remarks

Losses can be specified either as a string or a loss object. When loss is specified as one of these strings, the default values are used for the loss parameters. To change the default parameters, a loss object should be used, as seen in examples below.

Each trainer supports only a subset of the losses mentioned above. To get the supported losses and the default loss, please refer to the documentation page for the specific trainer.

The Poisson loss for regression. Assuming that the response variable y follows Poisson distribution, maximum likelihood is used to estimate the parameters by maximuzing the probability of obtaining the observed data. Its string name is 'poisson'.

It can be used for OnlineGradientDescentRegressor.