Supervised and Unsupervised Learning in R Programming Last Updated : 26 Jun, 2025 Comments Improve Suggest changes Like Article Like Report Machine Learning (ML) is a subset of Artificial Intelligence (AI) that enables computers to learn from data and improve their performance over time without being explicitly programmed. The choice of ML algorithms depends on the type of data and the task at hand which can be broadly divided into Supervised and Unsupervised Learning.Difference Between Labeled and Unlabeled DataIn the context of Machine Learning data can be divided into two types:Labeled Data: This type of data includes both the input and the corresponding output (also called the label). For example, in a dataset of house prices, each data point includes both the features (such as square footage, number of bedrooms, etc.) and the price (the label).Unlabeled Data: In this type of data only the input data is provided and there are no labels. The task of the machine learning algorithm is to find patterns, groupings or structure within the data. For example a dataset of customer transactions where there’s no information about which customers bought a particular product (labels).The distinction between these two types of data forms the foundation for Supervised and Unsupervised Learning.1. Supervised LearningSupervised learning involves training a model on labeled data where both the input data and the correct output (label) are provided. The goal is to predict the output for new or unseen data.Types of Supervised Learning:Classification: Predicts a categorical label (e.g. "disease" or "no disease").Regression: Predicts a continuous value (e.g. changes in "price", "weight", "temperature").Example: Linear Regression in RLinear Regression is a supervised learning algorithm used for predicting continuous values. We will use an example of implementing Simple Linear Regression in R. R install.packages('caTools') install.packages("ggplot2") library(caTools) library(ggplot2) dataset = mtcars lm.r = lm(mpg ~ wt, data = dataset) ypred = predict(lm.r, newdata = dataset) ggplot() + geom_point(aes(x = dataset$wt, y = dataset$mpg), colour = 'red') + ggtitle('MPG vs Weight Data') + xlab('Weight') + ylab('Miles per Gallon') ggplot() + geom_point(aes(x = dataset$wt, y = dataset$mpg), colour = 'red') + geom_line(aes(x = dataset$wt, y = predict(lm.r, newdata = dataset)), colour = 'blue') + ggtitle('MPG vs Weight With Regression Line') + xlab('Weight') + ylab('Miles per Gallon') Output:Mileage vs WeightMileage vs Weight and Regression Line2. Unsupervised LearningUnsupervised learning deals with datasets that are not labeled. The algorithm tries to identify hidden patterns or groupings in the data. There is no supervision, meaning the model does not know the "right" answers in advance.Types of Unsupervised Learning:Clustering: Groups data based on similarity (e.g., customer segmentation).Association: Finds relationships between variables (e.g., market basket analysis).Example: K-Means Clustering in RK-Means Clustering is an unsupervised algorithm that divides data into clusters based on similarity. We will use an example of implementing K-Means Clustering in R. R install.packages("cluster") library(cluster) data(iris) iris_1 <- iris[, -5] set.seed(240) kmeans.re <- kmeans(iris_1, centers = 3, nstart = 20) plot(iris_1[c("Sepal.Length", "Sepal.Width")], main="Unlabeled Data") y_kmeans <- kmeans.re$cluster clusplot(iris_1[, c("Sepal.Length", "Sepal.Width")], y_kmeans, lines = 0, shade = TRUE, color = TRUE, labels = 0, plotchar = FALSE, span = TRUE, main = paste("Cluster iris"), xlab = 'Sepal.Length', ylab = 'Sepal.Width') Output:Unlabeled DataK-Means ClusteringIn this article, we explored Supervised and Unsupervised Learning in R programming and understood how to decide which type of machine learning algorithm to use. Comment More infoAdvertise with us Next Article Introduction to Data in Machine Learning A AmiyaRanjanRout Follow Improve Article Tags : R Language AI-ML-DS R Machine-Learning AI-ML-DS With R Similar Reads Machine Learning with R Machine Learning is a growing field that enables computers to learn from data and make decisions without being explicitly programmed. It mimics the way humans learn from experiences, allowing systems to improve performance over time through data-driven insights. This Machine Learning with R Programm 3 min read Getting Started With Machine Learning In RIntroduction to Machine Learning in RMachine learning in R allows data scientists, analysts and statisticians to build predictive models, uncover patterns and gain insights using powerful statistical techniques combined with modern machine learning algorithms. 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