Feature Selection Using R | Machine Learning Models using Boruta Package

Introduction

This tutorial will guide you through the process of feature selection in machine learning using the Boruta package in R. Feature selection is crucial for improving model performance, particularly in scenarios involving large datasets. By the end of this guide, you will understand how to implement feature selection, build a Random Forest model, and make predictions using R.

Step 1: Install and Load Required Libraries

Before starting, make sure you have the necessary libraries installed. The primary library for feature selection in this tutorial is the Boruta package.

1. Open your R or RStudio.
2. Install the Boruta package if you haven't already:

   install.packages("Boruta")
   

3. Load the required libraries:

   library(Boruta)
   library(randomForest)
   

Step 2: Prepare Your Data

The next step involves preparing your dataset for analysis. Ensure your data is in an appropriate format.

1. Load your dataset into R. This can be done using:

   data <- read.csv("your_data_file.csv")
   

2. Inspect your data to understand its structure:

   str(data)
   summary(data)
   

Step 3: Feature Selection Using Boruta

Now that your data is ready, you can perform feature selection with the Boruta package.

1. Set the target variable and feature set. For example:

   target <- data$target_column
   features <- data[, -which(names(data) == "target_column")]
   

2. Run the Boruta function:

   boruta_result <- Boruta(target ~ ., data = features, doTrace = 2)
   

3. Review the results:

   print(boruta_result)
   

   • Look for confirmed and rejected features as indicated in the output.

Step 4: Handling Tentative Features

Sometimes, Boruta identifies tentative features that need further examination.

1. You can choose to remove tentative features or further investigate:

   boruta_final <- TentativeRoughFix(boruta_result)
   

2. Re-evaluate the final set of features:

   print(boruta_final)
   

Step 5: Data Partitioning

To build a machine learning model, you'll need to split your data into training and testing sets.

1. Use the caret package for data partitioning:

   install.packages("caret")
   library(caret)
   set.seed(123)
   train_index <- createDataPartition(data$target_column, p = 0.8, list = FALSE)
   train_data <- data[train_index, ]
   test_data <- data[-train_index, ]
   

Step 6: Build a Random Forest Model

With your training data ready, you can now build a Random Forest model.

1. Train the model using the selected features:

   rf_model <- randomForest(target ~ ., data = train_data, importance = TRUE)
   

2. Check the model's accuracy:

   print(rf_model)
   

Step 7: Make Predictions on Test Data

Finally, evaluate your model's performance by making predictions on the test dataset.

1. Use the trained model to predict the target variable:

   predictions <- predict(rf_model, newdata = test_data)
   

2. Assess the model's accuracy:

   confusionMatrix(predictions, test_data$target_column)
   

Conclusion

In this tutorial, you learned how to perform feature selection using the Boruta package in R, partition your dataset, and build a Random Forest model for predictions. Feature selection is a crucial step in developing efficient machine learning models, particularly when dealing with large datasets. To further enhance your skills, consider exploring other machine learning algorithms and feature selection techniques.