Loss Functions (qiskit_machine_learning.utils.loss_functions)

A collection of common loss functions to be used with the classifiers and regressors provided by Qiskit Machine Learning.

Loss Function Base Class

Loss	Abstract base class for computing Loss.
KernelLoss	Abstract base class for computing the loss of a kernel function.

Loss Functions

L1Loss	This class computes the L1 loss (i.e. absolute error) for each sample as:.
L2Loss	This class computes the L2 loss (i.e. squared error) for each sample as:.
CrossEntropyLoss	This class computes the cross entropy loss for each sample as:
SVCLoss	This class provides a kernel loss function for classification tasks by fitting an SVC model from scikit-learn.
SVRLoss	This class provides a kernel loss function for regression tasks by fitting an SVR model from scikit-learn.
MSRLoss	This class provides a simple mean squared regression loss function by fitting an SVR model from scikit-learn and computing the mean squared error on the training set.
MARLoss	This class provides a mean absolute regression loss function by fitting an SVR model from scikit-learn and computing the mean absolute error on the training set.
HuberLoss	This class provides a Huber loss function for regression.