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

18 August 2015

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

50 / 227 papers shown

Title
(Decision and regression) tree ensemble based kernels for regression and classification Dai Feng R. Baumgartner 25 2 0 19 Dec 2020
Two-Step Meta-Learning for Time-Series Forecasting Ensemble E. Vaičiukynas Paulius Danėnas Vilius Kontrimas Rimantas Butleris AI4TS 9 9 0 20 Nov 2020
Debiased Inverse Propensity Score Weighting for Estimation of Average Treatment Effects with High-Dimensional Confounders Yuhao Wang Rajen Dinesh Shah 100 15 0 17 Nov 2020
Early Anomaly Detection in Time Series: A Hierarchical Approach for Predicting Critical Health Episodes Vítor Cerqueira Luís Torgo Carlos Soares AI4TS 11 8 0 22 Oct 2020
Transparency, Auditability and eXplainability of Machine Learning Models in Credit Scoring Michael Bücker G. Szepannek Alicja Gosiewska P. Biecek FaML 22 109 0 28 Sep 2020
Random boosting and random^2 forests -- A random tree depth injection approach T. Krabel Thi Ngoc Tien Tran A. Groll Daniel Horn Carsten Jentsch 24 0 0 13 Sep 2020
Random Forest (RF) Kernel for Regression, Classification and Survival Dai Feng R. Baumgartner 23 3 0 31 Aug 2020
MementoML: Performance of selected machine learning algorithm configurations on OpenML100 datasets Wojciech Kretowicz P. Biecek 6 3 0 30 Aug 2020
Feature Selection Methods for Cost-Constrained Classification in Random Forests R. Jagdhuber Michel Lang Jörg Rahnenführer 11 1 0 14 Aug 2020
Estimating NBA players salary share according to their performance on court: A machine learning approach I. Papadaki M. Tsagris 6 1 0 29 Jul 2020
A unified machine learning approach to time series forecasting applied to demand at emergency departments Michaela Vollmer Ben Glampson T. Mellan Swapnil Mishra Luca Mercuri Ceire Costello R. Klaber G. Cooke Seth Flaxman Samir Bhatt OOD AI4TS 23 40 0 13 Jul 2020
Discovering Drug-Drug and Drug-Disease Interactions Inducing Acute Kidney Injury Using Deep Rule Forests Bowen Kuo Yihuang Kang Pinghsung Wu Sheng-Tai Huang Yajie Huang 5 2 0 04 Jul 2020
A General Machine Learning Framework for Survival Analysis Andreas Bender David Rügamer Fabian Scheipl B. Bischl CML 6 27 0 27 Jun 2020
A Survey of Machine Learning Methods and Challenges for Windows Malware Classification Edward Raff Charles K. Nicholas AAML 29 54 0 15 Jun 2020
Generalizing Gain Penalization for Feature Selection in Tree-based Models Bruna D. Wundervald Andrew C. Parnell Katarina Domijan 22 6 0 12 Jun 2020
Distributional Random Forests: Heterogeneity Adjustment and Multivariate Distributional Regression Domagoj Cevid Loris Michel Jeffrey Näf N. Meinshausen Peter Buhlmann 35 39 0 29 May 2020
Optimal survival trees ensemble Naz Gul Nosheen Faiz D. Brawn Rafał Kułakowski Zardad Khan B. Lausen 6 3 0 18 May 2020
Sequential Aggregation of Probabilistic Forecasts -- Applicaton to Wind Speed Ensemble Forecasts M. Zamo L. Bel O. Mestre AI4TS 12 19 0 07 May 2020
A framework for probabilistic weather forecast post-processing across models and lead times using machine learning Charlie Kirkwood T. Economou H. Odbert N. Pugeault 17 33 0 06 May 2020
DriveML: An R Package for Driverless Machine Learning Sayan Putatunda Dayanand Ubrangala Kiran Rama R. Kondapalli 6 0 0 01 May 2020
Multiple imputation using chained random forests: a preliminary study based on the empirical distribution of out-of-bag prediction errors Shangzhi Hong Yuqi Sun Hanying Li H. Lynn AI4CE 20 1 0 30 Apr 2020
Interpretable Random Forests via Rule Extraction Clément Bénard Gérard Biau Sébastien Da Veiga Erwan Scornet 20 58 0 29 Apr 2020
From unbiased MDI Feature Importance to Explainable AI for Trees Markus Loecher FAtt 16 5 0 26 Mar 2020
Large-scale benchmark study of survival prediction methods using multi-omics data Moritz Herrmann Philipp Probst R. Hornung V. Jurinovic A. Boulesteix 14 77 0 07 Mar 2020
Unbiased variable importance for random forests Markus Loecher FAtt 55 53 0 04 Mar 2020
Who Wins the Game of Thrones? How Sentiments Improve the Prediction of Candidate Choice Chaehan So 12 0 0 29 Feb 2020
Stochastic tree ensembles for regularized nonlinear regression Jingyu He Richard Hahn 12 30 0 09 Feb 2020
A Physiology-Driven Computational Model for Post-Cardiac Arrest Outcome Prediction Hanbiehn Kim Hieu Nguyen Qingchu Jin Sharmila Tamby Tatiana Gelaf Romer ... J. Greenstein J. Suarez C. Storm R. Winslow R. Stevens 39 6 0 09 Feb 2020
Explainable outlier detection through decision tree conditioning David Cortes 16 7 0 02 Jan 2020
Fair Data Adaptation with Quantile Preservation Drago Plečko N. Meinshausen 14 28 0 15 Nov 2019
Selective machine learning of doubly robust functionals Yifan Cui E. T. Tchetgen OOD 24 23 0 05 Nov 2019
Silas: High Performance, Explainable and Verifiable Machine Learning Hadrien Bride Zhe Hou Jie Dong J. Dong Seyedali Mirjalili 21 7 0 03 Oct 2019
Model-Agnostic Linear Competitors -- When Interpretable Models Compete and Collaborate with Black-Box Models Hassan Rafique Tong Wang Qihang Lin 14 4 0 23 Sep 2019
Super ensemble learning for daily streamflow forecasting: Large-scale demonstration and comparison with multiple machine learning algorithms Hristos Tyralis Georgia Papacharalampous A. Langousis AI4TS 18 12 0 09 Sep 2019
Machine learning for automatic construction of pseudo-realistic pediatric abdominal phantoms M. Virgolin Ziyuan Wang Tanja Alderliesten Peter A. N. Bosman 14 2 0 09 Sep 2019
Locally Optimized Random Forests T. Coleman K. Kaufeld M. Dorn L. Mentch 18 0 0 27 Aug 2019
Machine learning algorithms to infer trait-matching and predict species interactions in ecological networks Maximilian Pichler V. Boreux A. Klein M. Schleuning F. Hartig 16 100 0 26 Aug 2019
SIRUS: Stable and Interpretable RUle Set for Classification Clément Bénard Gérard Biau Sébastien Da Veiga Erwan Scornet 42 9 0 19 Aug 2019
Maize Yield and Nitrate Loss Prediction with Machine Learning Algorithms Mohsen Shahhosseini R. Martinez-Feria Guiping Hu S. Archontoulis 20 153 0 14 Aug 2019
On Explaining Machine Learning Models by Evolving Crucial and Compact Features M. Virgolin Tanja Alderliesten Peter A. N. Bosman 19 28 0 04 Jul 2019
A Debiased MDI Feature Importance Measure for Random Forests Xiao Li Yu Wang Sumanta Basu Karl Kumbier Bin Yu 27 83 0 26 Jun 2019
Linear Aggregation in Tree-based Estimators Sören R. Künzel Theo Saarinen Edward W. Liu Jasjeet Sekhon 23 10 0 15 Jun 2019
Hybrid Machine Learning Forecasts for the FIFA Women's World Cup 2019 A. Groll Christophe Ley G. Schauberger Hans Van Eetvelde A. Zeileis 17 6 0 03 Jun 2019
Evaluating time series forecasting models: An empirical study on performance estimation methods Vítor Cerqueira Luís Torgo I. Mozetič AI4TS 11 245 0 28 May 2019
Scalable and Efficient Hypothesis Testing with Random Forests T. Coleman Wei Peng L. Mentch 31 17 0 16 Apr 2019
High Dimensional Restrictive Federated Model Selection with multi-objective Bayesian Optimization over shifted distributions Xudong Sun Andrea Bommert Florian Pfisterer Jörg Rahnenführer Michel Lang B. Bischl FedML 19 12 0 24 Feb 2019
On the consistency of supervised learning with missing values Julie Josse Jacob M. Chen Nicolas Prost Erwan Scornet Gaël Varoquaux 28 113 0 19 Feb 2019
Survival Forests under Test: Impact of the Proportional Hazards Assumption on Prognostic and Predictive Forests for ALS Survival N. Korepanova H. Seibold Verena Steffen Torsten Hothorn 33 14 0 05 Feb 2019
Testing Conditional Independence in Supervised Learning Algorithms David S. Watson Marvin N. Wright CML 29 52 0 28 Jan 2019
Empowering individual trait prediction using interactions D. Gola I. König 43 5 0 25 Jan 2019