knitr::opts_chunk$set( collapse = TRUE, comment = "#>" )
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.
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