Articles - Classification Methods Essentials

Loading required R packages

• tidyverse for easy data manipulation
• caret for easy predictive modeling
• nnet for computing multinomial logistic regression

library(tidyverse)
library(caret)
library(nnet)

Preparing the data

We’ll use the iris data set, introduced in Chapter @ref(classification-in-r), for predicting iris species based on the predictor variables Sepal.Length, Sepal.Width, Petal.Length, Petal.Width.

We start by randomly splitting the data into training set (80% for building a predictive model) and test set (20% for evaluating the model). Make sure to set seed for reproducibility.

# Load the data
data("iris")
# Inspect the data
sample_n(iris, 3)
# Split the data into training and test set
set.seed(123)
training.samples <- iris$Species %>% 
  createDataPartition(p = 0.8, list = FALSE)
train.data  <- iris[training.samples, ]
test.data <- iris[-training.samples, ]

Computing multinomial logistic regression

# Fit the model
model <- nnet::multinom(Species ~., data = train.data)
# Summarize the model
summary(model)
# Make predictions
predicted.classes <- model %>% predict(test.data)
head(predicted.classes)
# Model accuracy
mean(predicted.classes == test.data$Species)

Model accuracy:

mean(predicted.classes == test.data$Species)

## [1] 0.967

Discussion

This chapter describes how to compute multinomial logistic regression in R. This method is used for multiclass problems. In practice, it is not used very often. Discriminant analysis (Chapter @ref(discriminant-analysis)) is more popular for multiple-class classification.