Linear Discriminant Analysis (LDA) in R

Linear Discriminant Analysis (LDA) in R

Are you looking for a simple, robust, and efficient method to help you with classification and dimensionality reduction? Do you want to learn how to implement and evaluate LDA in R programming, one of the most popular and powerful techniques for these tasks? If yes, then you have come to the right place. Linear discriminant analysis (LDA) is a supervised machine-learning technique that can be used for two main purposes: Classification and Dimensionality reduction.  # Fit the LDA model library(MASS) model <- lda(Species ~ ., data = train) # Print the model print(model) Table of Contents Key takeaways Linear discriminant analysis is a supervised machine-learning technique that can be used for classification and dimensionality reduction. It is based on finding the linear combinations of features that best separate the classes in the data set. It can be implemented in R programming using the lda function from MASS packages. It can be evaluated and compared with other methods using various met…