Basics
Tasks
Learners
Train
Predict
Preprocessing
Performance
Resampling
Tuning
Benchmark Experiments
Parallelization
Visualization
Use case - Regression
Advanced
Configuration
Wrapped Learners
Imputation
Generic Bagging
Advanced Tuning
Feature Selection/Filtering
Nested Resampling
Cost-Sensitive Classification
Imbalanced Classification Problems
ROC Analysis and Performance Curves
Multilabel Classification
Learning Curve Analysis
Partial Dependence Plots
Classifier Calibration
Hyperparameter Tuning Effects
Out-of-Bag Predictions
Handling of Spatial Data
Functional Data
Extending
Create Custom Learners
Create Custom Measures
Create Imputation Methods
Create Custom Filters
Appendix
Function Reference
News
Example Tasks
Integrated Learners
Implemented Measures
Integrated Filter Methods
mlr Publications
Talks, Videos and Workshops
mlr-org Packages
mlrMBO
mlrCPO
mlrHyperopt
OpenML
License
YEAR: 2013-2018 COPYRIGHT HOLDER: Bernd Bischl