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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

  • Spatial Data in the mlr3 Ecosystem

    Run a land cover classification of the city of Leipzig.

  • Recursive Feature Elimination on the Sonar Data Set

    Utilize the built-in feature importance of models.

  • Shadow Variable Search on the Pima Indian Diabetes Data Set

    Run a feature selection with permutated features.

  • Default Hyperparameter Configuration

    Run the default hyperparameter configuration of learners as a baseline.

  • Hotstarting

    Resume the training of learners.

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    Train and Evaluate Models

    • Basic Machine Learning on Iris data set

      Learn the basic operations train, predict, score, resample, and benchmark.

    • Imbalanced Data Handling

      Handle imbalanced data with oversampling, undersampling, and SMOTE imbalance correction.

    • Resampling - Stratified, Blocked and Predefined

      Apply stratified, block and custom resampling.

    • Factor Encoding

      Encode factor variables in a task.

    • German Credit Series

      Train, tune and pipeline different machine learning algorithms.

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    Optimize Models

    • Hyperparameter Optimization on the Palmer Penguins Data Set

      Optimize the hyperparameters of a classification tree with a few lines of code.

    • Introduction to the mlr3tuningspaces Package

      Apply predefined search spaces from scientific articles.

    • Early Stopping with XGBoost

      Simultaneously optimize hyperparameters and use early stopping.

    • Recursive Feature Elimination on the Sonar Data Set

      Utilize the built-in feature importance of models.

    • Hyperband Series

      Use the Hyperband optimizer with different budget parameters.

    • Practical Tuning Series

      Start with a tuned SVM and finish with a AutoML model.

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    Build Pipelines

    • A Pipeline for the Titanic Data Set

      Create new features and impute missing values with a pipeline.

    • Pipelines, Selectors, Branches

      Build a preprocessing pipeline with branching.

    • Target Transformations via Pipelines

      Transform the target variable.

    • Tuning a Complex Graph

      Tune a preprocessing pipeline and multiple tuners at once.

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    Apply Technical Tools and Run Special Tasks

    • Production Example Using Plumber and Docker

      Write a REST API using plumber and deploy it using Docker.

    • Visualization in mlr3

      Quickly plot objects of the mlr3 ecosystem.

    • Spatial Data in the mlr3 Ecosystem

      Run a land cover classification of the city of Leipzig.

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