**Course Title:** Mastering R Programming: Data Analysis, Visualization, and Beyond **Section Title:** Introduction to Machine Learning with R **Topic:** Implement a simple machine learning model using `caret` or `mlr3` and evaluate its performance.(Lab topic) **Objective:** In this lab topic, you will learn how to implement a simple machine learning model using the `caret` or `mlr3` package in R. You will also learn how to evaluate the performance of the model. By the end of this topic, you will be able to: * Implement a simple machine learning model using `caret` or `mlr3` * Evaluate the performance of the model using metrics such as accuracy, precision, recall, and F1 score * Use techniques such as cross-validation to improve the model's performance **Required Packages:** Before we begin, make sure you have the necessary packages installed and loaded in your R environment. You can install the required packages by running the following code: ```r install.packages("caret") install.packages("mlr3") library(caret) library(mlr3) ``` **Dataset:** For this lab, we will use the `iris` dataset, which is a built-in dataset in R. The `iris` dataset is a classic dataset that contains information about the sepal and petal lengths and widths of three species of iris (Iris setosa, Iris virginica, and Iris versicolor). ```r # Load the iris dataset data(iris) ``` **Implementing a Simple Machine Learning Model:** We will implement a simple linear regression model using `caret`. The goal is to predict the sepal length of an iris based on the sepal width. ```r # Split the dataset into training and testing sets set.seed(123) trainIndex <- createDataPartition(iris$Species, p = .7, list = FALSE) iris_train <- iris[trainIndex, ] iris_test <- iris[-trainIndex, ] # Train the model model <- lm(Sepal.Length ~ Sepal.Width, data = iris_train) # Print the model print(model) ``` This code trains a linear regression model using the training dataset and prints the coefficients. **Evaluating Model Performance:** To evaluate the performance of the model, we will use metrics such as mean absolute error (MAE) and mean squared error (MSE). ```r # Make predictions on the testing dataset predictions <- predict(model, iris_test) # Evaluate the performance of the model MAE <- mean(abs(predictions - iris_test$Sepal.Length)) MSE <- mean((predictions - iris_test$Sepal.Length)^2) # Print the metrics print(paste("MAE:", MAE)) print(paste("MSE:", MSE)) ``` This code makes predictions on the testing dataset and calculates the MAE and MSE. **Implementing a Simple Machine Learning Model using `mlr3`:** Alternatively, you can also implement a simple machine learning model using `mlr3`. Here is an example: ```r # Split the dataset into training and testing sets set.seed(123) trainIndex <- createDataPartition(iris$Species, p = .7, list = FALSE) iris_train <- iris[trainIndex, ] iris_test <- iris[-trainIndex, ] # Define the problem problem <- mlr_task(iris_train, target = "Sepal.Length") # Train the model model <- mlr_vm(modeltype = "linear_regression", target_vars = "Sepal.Length") # Train the model on the training dataset learner <- makelearner("regression", task = problem) fit <- train(learner, iris_train) # Make predictions on the testing dataset predictions <- predict(fit, iris_test) # Evaluate the performance of the model MAE <- mean(abs(predictions - iris_test$Sepal.Length)) MSE <- mean((predictions - iris_test$Sepal.Length)^2) # Print the metrics print(paste("MAE:", MAE)) print(paste("MSE:", MSE)) ``` This code defines a problem using `mlr_task`, trains a linear regression model using `makelearner`, and makes predictions on the testing dataset. **Conclusion:** In this lab topic, you learned how to implement a simple machine learning model using `caret` and `mlr3`. You also learned how to evaluate the performance of the model using metrics such as MAE and MSE. Remember to use techniques such as cross-validation to improve the model's performance. **Additional Resources:** For more information on `caret`, please visit the [caret GitHub page](https://github.com/topepo/caret/). For more information on `mlr3`, please visit the [mlr3 GitHub page](https://github.com/mlr-org/mlr3/). **Assignment:** Implement a simple machine learning model using either `caret` or `mlr3` on a dataset of your choice. Evaluate the performance of the model using metrics such as accuracy, precision, recall, and F1 score. **Leave a comment:** If you have any questions or need help with this lab, please leave a comment below. **What's next:** In our next topic, we will learn about handling large datasets in R using `data.table` and `dplyr`.