SSLR contains models created by developers and wrappers of different packages such as RSSL. From RSSL, we use S3VM methods.
The list of models is:
Classification: SelfTraining(),SSLRDecisionTree(), SSLRRandomForest(), triTraining(), coBC(), democratic(), EMLeastSquaresClassifierSSLR(), EMNearestMeanClassifierSSLR(), EntropyRegularizedLogisticRegressionSSLR(), LaplacianSVMSSLR(), LinearTSVMSSLR(), WellSVMSSLR(), MCNearestMeanClassifierSSLR(), oneNN(), setred(), snnrce(), TSVMSSLR(), USMLeastSquaresClassifierSSLR(), GRFClassifierSSLR()
Regression: coBC(),COREG(), SSLRDecisionTree(), SSLRRandomForest()
Clustering: constrained_kmeans(), seeded_kmeans(), ckmeansSSLR(), cclsSSLR(), mpckmSSLR(), lcvqeSSLR()
NOTE: In the Regression modelling section we can see more examples of use in regression tasks. In Decision Tree , Random Forest and coBC we only have examples for classification tasks.
NOTE: In the Clustering modelling section we can see how to plot clusters with factoextra package.