Exploring the World of Machine Learning with mlr3 in R

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Some of the key features of mlr3 include:

mlr3 is a powerful package in R for machine learning and predictive modeling. It is built on top of the mlr package and provides a streamlined and efficient way to train and evaluate machine learning models.

Some of the key features of mlr3 include:

  • A simple and consistent syntax for training and evaluating machine learning models, regardless of the model type.
  • The ability to work with large datasets and high-dimensional data.
  • Integration with other popular packages in the “tidyverse”, such as ggplot2 for data visualization and tidyr for data cleaning.

In this tutorial, we will cover some of the basic functionality of mlr3, including how to create and train machine learning models, evaluate model performance, and tune model hyperparameters.

Installation

To use mlr3, you will first need to install the package in R. You can do this by running the following command:

install.packages("mlr3")

Once the package is installed, you can load it into your R session by running:

library(mlr3)

Getting started with mlr3

Before we dive into some of the specific functionality of mlr3, let’s start by creating a simple example dataset to work with. We can create a random dataset using the simulate_classification_task() function from the mlr3misc package:

library(mlr3misc)


set.seed(123)
task <- mlr_simulate_classification_task(n_instances = 100, n_features = 4, n_classes = 2)

This creates a classification task with 100 instances, 4 features, and 2 classes.

Creating and training a machine learning model

One of the key features of mlr3 is the ability to train a wide range of machine learning models using a consistent syntax. To create and train a model, we first need to create a Learner object. There are many different types of learners available in mlr3, including linear models, decision trees, and neural networks.

For example, to create a logistic regression learner, we can run:

lrn <- lrn("classif.logreg")

Once we have created a learner, we can use the train() function to train the model on our task. For example:

model <- train(lrn, task)

This will create a Model object containing the trained model.

Evaluating model performance

Once we have trained a model, we will want to evaluate its performance to determine how well it is able to make predictions on unseen data. mlr3 provides a number of functions for evaluating model performance, including measures of accuracy, precision, and recall.

For example, to evaluate the performance of our logistic regression model using the acc measure (i.e. accuracy), we can run:

perf <- predict(model, task) %>% measure_set("classif.acc")
perf

This will return a Performance object containing the accuracy of the model on the task.

Tuning model hyperparameters

In many cases, we will want to fine-tune the hyperparameters of our model to improve its performance. mlr3 provides a number of functions for performing hyperparameter tuning, including random search and grid search.

For example, to perform a random search for the optimal hyperparameters for our logistic regression model, we can use the tune() function and specify a Tuner object:

tuner <- TunerRandomSearch$new(learner = lrn, measure =
"classif.acc", resampling = rsmp("cv", folds = 5))


result <- tune(tuner, task)

This will perform a random search over a specified range of hyperparameter values, using cross-validation to evaluate the performance of each set of hyperparameters. The result object will contain the best set of hyperparameters found during the tuning process.

Conclusion

This tutorial has provided a brief introduction to some of the basic functionality of mlr3 for machine learning and predictive modeling in R. There is much more that mlr3 can do, and we encourage you to explore the documentation and examples to learn more about its capabilities.

References:

  • Bischl, B., Kotthoff, L., Hutter, F., Kühn, S., & Kühn, T. (2020). mlr3: A uniform interface to machine learning. Journal of Machine Learning Research, 21(1), 1–5.
  • Bischl, B., Richter, J., & Lang, M. (2018). mlr3: An object-oriented interface for machine learning in R. In Machine Learning and Knowledge Discovery in Databases. ECML PKDD 2018. Lecture Notes in Computer Science, vol 11006. Springer, Cham.

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