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Computes the hinge loss between y_true and y_pred.​ Type : polymorphic.




Input parameters


Hinge in : class
reduction : enum, type of reduction to apply to the loss. In almost all cases this should be “Sum over Batch“.


Output parameters


Hinge out : class

Required data

Β y_pred :Β array,Β predicted values.
Β y_true :Β array,Β true values are expected to be -1 or 1. If binary (0 or 1) labels are provided we will convert them to -1 or 1.

Use cases

Hinge loss is a loss function primarily used in binary classification tasks, such as detecting the presence or absence of an object.

This function aims to maximize the margin between prediction categories. It is widely used in Support Vector Machines (SVM) because it pushes the model to not only be correct, but to be confident in its prediction with a clear margin between classes.


All these exemples are snippets PNG, you can drop these Snippet onto the block diagram and get the depicted code added to your VI (Do not forget to install HAIBAL library to run it).

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