mlr-org 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

mlr Publications

Source: vignettes/tutorial/mlr_publications.Rmd
mlr_publications.Rmd

An overview of our mlr related publications:

Journal Title Description Citation
jlmr mlr: Machine Learning in R Software paper about mlr bibtex
arxiv mlrMBO: A Modular Framework for Model-Based Optimization of Expensive Black-Box Functions Bayesian optimization with mlrMBO bibtex
Journal of Statistical Computation and Simulation Automatic model selection for high-dimensional survival analysis Tuning with iterated F-Racing bibtex
Springer On Class Imbalance Correction for Classification Algorithms in Credit Scoring Class imbalance correction algorithms bibtex
R Journal Multilabel Classification with R Package mlr Benchmark on multilabel classification algorithms bibtex
Springer OpenML: An R Package to Connect to the Machine Learning Platform OpenML Software paper about OpenML bibtex
JOSS iml: An R package for Interpretable Machine Learning Software paper about iml bibtex

Developed by Bernd Bischl, Michel Lang, Lars Kotthoff, Patrick Schratz, Julia Schiffner, Jakob Richter, Zachary Jones, Giuseppe Casalicchio, Mason Gallo.

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