mlsanityData to 7 Oct 2026

ranger: A Fast Implementation of Random Forests for High Dimensional Data in C++ and R

arXiv 1508.04409 ↗First posted 18 Aug 2015stat.ML stat.COcited by 171

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Abstract (arXiv)

We introduce the C++ application and R package ranger. The software is a fast implementation of random forests for high dimensional data. Ensembles of classification, regression and survival trees are supported. We describe the implementation, provide examples, validate the package with a reference implementation, and compare runtime and memory usage with other implementations. The new software proves to scale best with the number of features, samples, trees, and features tried for splitting. Finally, we show that ranger is the fastest and most memory efficient implementation of random forests to analyze data on the scale of a genome-wide association study.

Key facts. Cited by 171 papers in the map (data through 7 Oct 2026). First posted on arXiv 18 Aug 2015.

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Citing papers per year: 2021: 38; 2022: 31; 2023: 32; 2024: 31; 2025: 23; 2026: 16.

171 papers.

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  1. 2021Hyperparameter Optimization: Foundations, Algorithms, Best Practices and Open Challengescited by 143
  2. 2024Do LLMs write like humans? Variation in grammatical and rhetorical stylescited by 62
  3. 2021Machine learning methods for postprocessing ensemble forecasts of wind gusts: A systematic comparisoncited by 34
  4. 2023Kriging Convolutional Networkscited by 30
  5. 2022Geometry- and Accuracy-Preserving Random Forest Proximitiescited by 24
  6. 2021SHAFF: Fast and consistent SHApley eFfect estimates via random Forestscited by 22
  7. 2021groupShapley: Efficient prediction explanation with Shapley values for feature groupscited by 21
  8. 2021Regularized target encoding outperforms traditional methods in supervised machine learning with high cardinality featurescited by 20
  9. 2021Using Shapley Values and Variational Autoencoders to Explain Predictive Models with Dependent Mixed Featurescited by 19
  10. 2022Hierarchical Shrinkage: improving the accuracy and interpretability of tree-based methodscited by 18
  11. 2021fairadapt: Causal Reasoning for Fair Data Pre-processingcited by 16
  12. 2024Self-Calibrating Conformal Predictioncited by 16
38 more
  1. 2023A Comparative Study of Methods for Estimating Conditional Shapley Values and When to Use Themcited by 15
  2. 2021DoubleML -- An Object-Oriented Implementation of Double Machine Learning in Rcited by 13
  3. 2022Yggdrasil Decision Forests: A Fast and Extensible Decision Forests Librarycited by 12
  4. 2022Machine Learning and Deep Learning -- A review for Ecologistscited by 11
  5. 2023One-Step Estimation of Differentiable Hilbert-Valued Parameterscited by 11
  6. 2024Statistical Multicriteria Benchmarking via the GSD-Frontcited by 11
  7. 2021MDA for random forests: inconsistency, and a practical solution via the Sobol-MDAcited by 10
  8. 2021Explaining predictive models using Shapley values and non-parametric vine copulascited by 9
  9. 2021fairmodels: A Flexible Tool For Bias Detection, Visualization, And Mitigationcited by 9
  10. 2021A cautionary tale on fitting decision trees to data from additive models: generalization lower boundscited by 9
  11. 2021Avoiding C-hacking when evaluating survival distribution predictions with discrimination measurescited by 7
  12. 2022GP-BART: a novel Bayesian additive regression trees approach using Gaussian processescited by 7
  13. 2023Adaptive debiased machine learning using data-driven model selection techniquescited by 7
  14. 2021On Wasted Contributions: Understanding the Dynamics of Contributor-Abandoned Pull Requestscited by 6
  15. 2022Ultra-marginal Feature Importance: Learning from Data with Causal Guaranteescited by 6
  16. 2022Comparison of machine learning algorithms for merging gridded satellite and earth-observed precipitation datacited by 6
  17. 2021Test for non-negligible adverse shiftscited by 5
  18. 2022The Infinitesimal Jackknife and Combinations of Modelscited by 5
  19. 2023Merging satellite and gauge-measured precipitation using LightGBM with an emphasis on extreme quantilescited by 5
  20. 2023Depth Functions for Partial Orders with a Descriptive Analysis of Machine Learning Algorithmscited by 5
  21. 2024Improving the Weighting Strategy in KernelSHAPcited by 5
  22. 2025shapr: Explaining Machine Learning Models with Conditional Shapley Values in R and Pythoncited by 5
  23. 2025Tree Ensemble Explainability through the Hoeffding Functional Decomposition and TreeHFD Algorithmcited by 5
  24. 2021Meta-Learning for Symbolic Hyperparameter Defaultscited by 4
  25. 2021mlr3spatiotempcv: Spatiotemporal resampling methods for machine learning in Rcited by 4
  26. 2021Automated Benchmark-Driven Design and Explanation of Hyperparameter Optimizerscited by 4
  27. 2022Meta-Learners for Estimation of Causal Effects: Finite Sample Cross-Fit Performancecited by 4
  28. 2022A Simple Unified Approach to Testing High-Dimensional Conditional Independences for Categorical and Ordinal Datacited by 4
  29. 2022Comparison of tree-based ensemble algorithms for merging satellite and earth-observed precipitation data at the daily time scalecited by 4
  30. 2023Comparison of Methods that Combine Multiple Randomized Trials to Estimate Heterogeneous Treatment Effectscited by 4
  31. 2024Interpretable Machine Learning for Survival Analysiscited by 4
  32. 2021Model Selection for Time Series Forecasting: Empirical Analysis of Different Estimatorscited by 3
  33. 2021Mutation is all you needcited by 3
  34. 2021Using Sequential Statistical Tests for Efficient Hyperparameter Tuningcited by 3
  35. 2022Time series features for supporting hydrometeorological explorations and predictions in ungauged locations using large datasetscited by 3
  36. 2022Classification and mapping of low-statured 'shrubland' cover types in post-agricultural landscapes of the US Northeastcited by 3
  37. 2022Fine-resolution landscape-scale biomass mapping using a spatiotemporal patchwork of LiDAR coveragescited by 3
  38. 2022Accelerated and interpretable oblique random survival forestscited by 3

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