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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.
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.