Unlocking the Power of RandomForest with the Sonar Dataset: An In-Depth Analysis

Introduction

Random Forest is a renowned ensemble learning technique used for classification and regression. This method constructs a multitude of decision trees at training time and outputs the mode of the classes or mean prediction of the individual trees for classification and regression tasks, respectively. In this comprehensive article, we will use the `randomForest`, `mlbench`, and `caret` packages in R to demonstrate the implementation of Random Forest with the Sonar dataset. We will also explore how to optimize the Random Forest model through parameter tuning.

Preliminary Setup

Before we delve into the coding aspect, it is essential to load the necessary libraries and dataset. For this demonstration, the Sonar dataset from the `mlbench` package is utilized. This dataset is commonly used for binary classification tasks and comprises 208 instances and 61 attributes.

```R
library(randomForest)
library(mlbench)
library(caret)
data(Sonar)
```

Dataset Preparation

After loading the Sonar dataset, we need to segregate it into features and target variable. The features are stored in variable `x` while the target variable is stored in `y`.

```R
dataset <- Sonar
x <- dataset[,1:60]
y <- dataset[,61]
```

Model Creation with Default Parameters

Initially, we construct a Random Forest model with default parameters. To evaluate the model’s performance, repeated cross-validation is used, with the accuracy metric serving as the performance measure.

```R
control <- trainControl(method="repeatedcv", number=10, repeats=3)
seed <- 7
metric <- "Accuracy"
set.seed(seed)
mtry <- sqrt(ncol(x))
tunegrid <- expand.grid(.mtry=mtry)
rf_default <- train(Class~., data=dataset, method="rf", metric=metric, tuneGrid=tunegrid, trControl=control)
print(rf_default)
```

Model Optimization through Grid Search

To enhance the model’s predictive accuracy, we implement grid search for hyperparameter tuning. Grid search is a traditional technique for hyperparameter tuning where a grid of hyperparameter values is specified and the model is trained for each combination of parameters.

```R
control <- trainControl(method="repeatedcv", number=10, repeats=3, search="grid")
set.seed(seed)
tunegrid <- expand.grid(.mtry=c(1:15))
rf_gridsearch <- train(Class~., data=dataset, method="rf", metric=metric, tuneGrid=tunegrid, trControl=control)
print(rf_gridsearch)
plot(rf_gridsearch)
```

Code Refactoring

For improved readability and maintenance, let’s refactor the initial code. Refactoring involves restructuring existing code without changing its external behavior. The goal is to improve the nonfunctional attributes of the software, making it easier to comprehend, reducing its complexity, and increasing its maintainability.

Below is the refactored version of the initial code:

```R
library(randomForest)
library(mlbench)
library(caret)

# Load and Prepare Dataset
load_and_prepare_data <- function() {
data(Sonar)
dataset <- Sonar
x <- dataset[,1:60]
y <- dataset[,61]
list(x = x, y = y, dataset = dataset)
}

# Train model with default parameters
train_default_model <- function(x, dataset) {
control <- trainControl(method="repeatedcv", number=10, repeats=3)
seed <- 7
metric <- "Accuracy"
set.seed(seed)
mtry <- sqrt(ncol(x))
tunegrid <- expand.grid(.mtry=mtry)
train(Class~., data=dataset, method="rf", metric=metric, tuneGrid=tunegrid, trControl=control)
}

# Train model with grid search
train_grid_search_model <- function(dataset) {
control <- trainControl(method="repeatedcv", number=10, repeats=3, search="grid")
seed <- 7
set.seed(seed)
tunegrid <- expand.grid(.mtry=c(1:15))
train(Class~., data=dataset, method="rf", metric="Accuracy", tuneGrid=tunegrid, trControl=control)
}

# Main function to run the code
main <- function() {
data <- load_and_prepare_data()
rf_default <- train_default_model(data$x, data$dataset)
print(rf_default)

rf_gridsearch <- train_grid_search_model(data$dataset)
print(rf_gridsearch)
plot(rf_gridsearch)
}

# Run the main function
main()
```

Conclusion

Through this in-depth article, we’ve walked through the process of implementing and optimizing a Random Forest model using the Sonar dataset in R. The refactored code provided is more readable and maintainable, making it a valuable resource for data science practitioners looking to harness the power of Random Forest for their classification tasks. The combination of the `randomForest`, `mlbench`, and `caret` packages offers a robust framework for developing and fine-tuning machine learning models efficiently and effectively.

Essential Gigs