1
Prerequisites
2
Introduction
3
Building Blocks
3.1
Tasks
3.1.1
Task Types
3.1.2
Task Creation
3.1.3
Predefined tasks
3.1.4
Task API
3.2
Learners
3.2.1
Predefined Learners
3.2.2
Train and Predict
4
Experiments
4.1
Task and learner objects
4.2
Index vector for train/test splits
4.3
Setting up an experiment
4.4
Training
4.5
Predicting
4.5.1
Performance assessment
4.5.2
Chaining methods
5
Resampling
5.1
Objects
5.2
Resampling
5.3
Manual instantiation
5.4
Custom resampling
6
Benchmarking
6.1
Benchmarking Exhaustive Designs
6.2
Converting specific benchmark objects to resample objects
7
Error Handling
7.1
Conditions
7.2
Encapsulation
7.3
Fallback learners
8
Parallelization
References
Introduction to mlr3
5
Resampling