SSLR provides a collection of techniques for semi-supervised classification, regression and clustering. In semi-supervised problems, both labeled and unlabeled data are used to train a model. The package includes a collection of semi-supervised learning techniques: self-training, co-training, democratic, decision tree, random forest, S3VM … etc, with a fairly intuitive interface that is easy to use.
This package uses RSSL package for build models with S3VMs.
This package has been designed and implemented to solve semi-supervised learning problems. Using data that is not labeled can improve the accuracy of the predictions.
With few lines of code we will be able to give different solutions with a simple and intuitive interface.
Installation
# Install release version from CRAN install.packages("SSLR")
Usage (some models)
library(tidyverse) library(tidymodels) #Load a lot of packages like parsnip library(caret) #For creating data library(SSLR) data(wine) set.seed(1) train.index <- createDataPartition(wine$Wine, p = .7, list = FALSE) train <- wine[ train.index,] test <- wine[-train.index,] cls <- which(colnames(wine) == "Wine") #5 % LABELED DATA labeled.index <- createDataPartition(wine$Wine, p = .05, list = FALSE) train[-labeled.index,cls] <- NA #With decision tree from rpart package dt <- decision_tree(mode = "classification", tree_depth = 10) %>% set_engine("rpart") #Models using dt m <- SSLRDecisionTree(w = 0.3) %>% fit(Wine ~ ., data = train) m <- selfTraining(learner = dt) %>% fit(Wine ~ ., data = train) m <- coBC(learner = dt,N = 10,perc.full = 0.6) %>% fit(Wine ~ ., data = train) m <- triTraining(learner = dt) %>% fit(Wine ~ ., data = train) m <- setred(learner = dt) %>% fit(Wine ~ ., data = train)
