demo/TSVM.R

In SSLR: Semi-Supervised Classification, Regression and Clustering Methods

library(tidyverse)
library(caret)
library(tidymodels)
library(SSLR)

data(breast)

set.seed(1)
train.index <- createDataPartition(breast$Class, p = .7, list = FALSE)
train <- breast[ train.index,]
test  <- breast[-train.index,]

cls <- which(colnames(breast) == "Class")

#% LABELED
labeled.index <- createDataPartition(breast$Class, p = .2, list = FALSE)
train[-labeled.index,cls] <- NA

library(kernlab)
m <- TSVMSSLR(kernel = kernlab::vanilladot()) %>% fit(Class ~ ., data = train)


#Accesing model from RSSL
model <- m$model