An R package for semi-supervised regression.
The ssr package implements Co-training by Committee and self-learning semi-supervised learning (SSL) algorithms for regression. In semi-supervised learning, algorithms learn model's parameters not only from labeled data but also from unlabeled data. In many applications, it is difficult, expensive, time-consuming, etc. to label data. Thus, semi-supervised methods learn by combining the limited labeled data points and the unlabeled data points.
The ssr package provides the following functionalities:
- Train Co-training by Committee models.
- Train self-learning models.
- Track and plot performance during training.
- Generate plots to quickly visualize the results.
- User can specify the base regressors to be used by the Co-training committee and self-learning from the caret package, other packages or custom functions.
You can install the ssr package from CRAN:
install.packages("ssr")
or you can install the development version from GitHub.
# install.packages("devtools")
devtools::install_github("enriquegit/ssr")
The following example shows how to train a Co-training Committee of two regressors: a linear model and a KNN.
library(ssr)
dataset <- friedman1 # Load friedman1 dataset.
set.seed(1234)
# Prepare de data
split1 <- split_train_test(dataset, pctTrain = 70)
split2 <- split_train_test(split1$trainset, pctTrain = 5)
L <- split2$trainset
U <- split2$testset[, -11] # Remove the labels.
testset <- split1$testset
# Define list of regressors.
regressors <- list(linearRegression=lm, knn=caret::knnreg)
# Fit the model.
model <- ssr("Ytrue ~ .", L, U, regressors = regressors, testdata = testset)
# Plot RMSE.
plot(model)
# Get the predictions on the testset.
predictions <- predict(model, testset)
# Calculate RMSE on the test set.
sqrt(mean((predictions - testset$Ytrue)^2))
For detailed explanations and more examples refer to the package vignettes.
To cite package ssr in publications use:
Enrique Garcia-Ceja (2019). ssr: Semi-Supervised Regression Methods.
R package https://CRAN.R-project.org/package=ssr
BibTex entry for LaTeX:
@Manual{enriqueSSR,
title = {ssr: Semi-Supervised Regression Methods},
author = {Enrique Garcia-Ceja},
year = {2019},
note = {R package},
url = {https://CRAN.R-project.org/package=ssr},
}