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mlr
v2.19.0
Basics
Task
Learner
Train
Predict
Preprocessing
Tuning
Resampling
Benchmarking
Parallelization
Performance
Visualization
Use case - Regression
Advanced
mlr Configuration
Wrapped Learners
Imputation
Generic Bagging
Advanced Tuning
Feature Selection/Filtering
Nested Resampling
Imbalanced Classification Problems
ROC Analysis and Performance Curves
Learning Curve Analysis
Partial Dependence Plots
Classifier Calibration
Hyperparameter Tuning Effects
Out-of-Bag Predictions
Multilabel Classification
Cost-Sensitive Classification
Spatial Data
Functional Data
Extending
Create Custom Learners
Create Custom Measures
Create Custom Imputation Methods
Create Custom Filters
Appendix
Integrated Tasks
Integrated Learners
Integrated Measures
Integrated Filter Methods
mlr Publications
Talks, Videos and Workshops
mlrMBO
mlrCPO
mlrHyperopt
OpenML
Changelog
Reference
Release notes
Version 2.19
Version dev
Version 2.18
Version 2.17
Version 2.16
Version 2.15
Version 2.14
Version 2.13
Version 2.12
Version 2.11
Version 2.10
Version 2.9
Version 2.8
Version 2.7
Version 2.6
Version 2.5
Version 2.4
Version 2.3
Version 2.2
Version 2.1
Version 2.0
Version 1.1
