SVRSVR# class sklearn.svm.SVR(*, kernel='rbf', degree=3, gamma='scale', coef0=0.0, tol=0.001, C=1.0, epsilon=0.1, shrinking=True, cache_size=200, verbose=False, max_iter=-1)[source]# Epsilon-Support Vector Regression. The free parameters in the model are C and epsilon. The implementation is based on libsvm. The fit time complexity is more than quadratic with the number of samples which makes it hard
Comparison of kernel ridge regression and SVR — scikit-learn 0.22.2 documentation
STUDY OF Ε-SMOOTH SUPPORT VECTOR REGRESSION AND COMPARISON WITH Ε- SUPPORT VECTOR REGRESSION AND POTENTIAL SUPPORT VECTOR MACHINES FOR PREDICTION FOR THE ANTITUBERCULAR ACTIVITY OF OXAZOLINES AND OXAZOLES DERIVATIVES
