culebra.fitness_function.svc_optimization
module¶
Fitness functions for parameter optimization problems.
This sub-module provides several fitness functions intended to optimize the Support Vector Classifier (SVC) hyperparameters for a given dataset. The following fitness functions are provided:
C
: Dummy single-objective function that minimizes the regularization parameter C of a SVM-based classifier with RBF kernels.
KappaIndex
: Single-objective function that maximizes the Kohen’s Kappa index for a SVM-based classifier with RBF kernels.
Accuracy
: Single-objective function that maximizes the Accuracy for a SVM-based classifier with RBF kernels.
KappaC
: Bi-objective function that tries to both maximize the Kohen’s Kappa index and minimize the regularization parameter C of a SVM-based classifier with RBF kernels.
AccuracyC
: Bi-objective function that tries to both maximize the accuracy and minimize the regularization parameter C of a SVM-based classifier with RBF kernels.