Gallery
In the gallery, you find case studies and demos. The posts are mostly about specific features and cover advanced topics. If you are completely new to mlr3 or machine learning, you should start with the book. Pick a post from one of the four categories or browse all posts sorted by date.
Latest
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Wrapper-based Ensemble Feature Selection
Find the most stable and predictive features using multiple learners and resampling techniques.
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Time constraints in the mlr3 ecosystem
Set time limits for learners, tuning and nested resampling.
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Analyzing the Runtime Performance of tidymodels and mlr3
Compare the runtime performance of tidymodels and mlr3.
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Survival modeling in mlr3 using Bayesian Additive Regression Trees (BART)
Demonstrate use of survival BART on the lung dataset via mlr3proba and distr6.
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Spatial Data in the mlr3 Ecosystem
Run a land cover classification of the city of Leipzig.
Train and Evaluate Models
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Imbalanced Data Handling
Handle imbalanced data with oversampling, undersampling, and SMOTE imbalance correction.
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Resampling - Stratified, Blocked and Predefined
Apply stratified, block and custom resampling.
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Factor Encoding
Encode factor variables in a task.
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German Credit Series
Train, tune and pipeline different machine learning algorithms.
Optimize Models
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Recursive Feature Elimination on the Sonar Data Set
Utilize the built-in feature importance of models.
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Hyperband Series
Use the Hyperband optimizer with different budget parameters.
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Practical Tuning Series
Start with a tuned SVM and finish with a AutoML model.
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Default Hyperparameter Configuration
Run the default hyperparameter configuration of learners as a baseline.
Build Pipelines
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A Pipeline for the Titanic Data Set
Create new features and impute missing values with a pipeline.
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Pipelines, Selectors, Branches
Build a preprocessing pipeline with branching.
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Target Transformations via Pipelines
Transform the target variable.
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Tuning a Complex Graph
Tune a preprocessing pipeline and multiple tuners at once.
Apply Technical Tools and Run Special Tasks
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Time constraints in the mlr3 ecosystem
Set time limits for learners, tuning and nested resampling.
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Production Example Using Plumber and Docker
Write a REST API using plumber and deploy it using Docker.
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Visualization in mlr3
Quickly plot objects of the mlr3 ecosystem.
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Spatial Data in the mlr3 Ecosystem
Run a land cover classification of the city of Leipzig.
Exercise Collection for Practice and Learning
This curated exercise collection offers hands-on, solvable exercises focused on core concepts, from basic modeling and resampling to tuning and advanced techniques. Each exercise follows a consistent structure: clear learning goal, prerequisites, structured task list with hints how to solve them, collapsible password-protected solutions, and key take-aways designed to support systematic practice and self-paced learning.
See all exercises