# List of models

`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.