SmoothedHinge 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

SmoothedHinge

Constructor

SmoothedHinge(smoothing_const=1.0)

Parameters

smoothing_const

Smoothing constant

Examples

   ###############################################################################
   # Smoothed Hinge Loss
   from nimbusml.linear_model import FastLinearBinaryClassifier
   # can also use loss class instead of string
   from nimbusml.loss import SmoothedHinge

   # specifying the loss function as a string keyword
   trainer1 = FastLinearBinaryClassifier(loss='smoothed_hinge')

   # equivalent to loss='smoothed_hinge'
   trainer2 = FastLinearBinaryClassifier(loss=SmoothedHinge())
   trainer3 = FastLinearBinaryClassifier(loss=SmoothedHinge(smoothing_const=0.5))

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 Smoothed hinge loss for classification. Its string name is 'smoothed_hinge'.

It can be used for AveragedPerceptronBinaryClassifier, FastLinearBinaryClassifier, FastLinearClassifier, SgdBinaryClassifier.