| Hyperparameter tuning and performance assessment of statistical and machine-learning algorithms using spatial data P Schratz, J Muenchow, E Iturritxa, J Richter, A Brenning Ecological Modelling 406, 109-120, 2019 | 454* | 2019 |
| mlr3: A modern object-oriented machine learning framework in R M Lang, M Binder, J Richter, P Schratz, F Pfisterer, S Coors, Q Au, ... Journal of Open Source Software 4 (44), 1903, 2019 | 318* | 2019 |
| Predicting forest cover in distinct ecosystems: The potential of multi-source Sentinel-1 and-2 data fusion K Heckel, M Urban, P Schratz, MD Mahecha, C Schmullius Remote Sensing 12 (2), 302, 2020 | 62 | 2020 |
| The performance of landslide susceptibility models critically depends on the quality of digital elevation models J Brock, P Schratz, H Petschko, J Muenchow, M Micu, A Brenning Geomatics, Natural Hazards and Risk 11 (1), 1075-1092, 2020 | 50 | 2020 |
| RQGIS: Integrating R with QGIS for Statistical Geocomputing. J Muenchow, P Schratz, A Brenning R Journal 9 (2), 2017 | 36 | 2017 |
| parallelMap: Unified interface to parallelization back-ends B Bischl, M Lang, P Schratz R package version 1, 2015 | 32 | 2015 |
| package’oddsratio’: Odds ratio calculation for GAM (M) s & GLM (M) s PR Schratz R Foundation for Statistical Computing: Vienna, Austria, 2017 | 30* | 2017 |
| mlr3verse: Easily install and load the’mlr3’package family M Lang, P Schratz R package version 0.2 1, 2021 | 19 | 2021 |
| FSelector: selecting attributes. R package Version 0.19 P Romanski, L Kotthoff, P Schratz R Core Development Team, 2018 | 18 | 2018 |
| Package ‘RSAGA’ A Brenning, D Bangs, M Becker, P Schratz, F Polakowski The Comprehensive R Archive Network https://CRAN. R-project. org/package= RSAGA, 2018 | 17 | 2018 |
| Woody cover mapping in the savanna ecosystem of the Kruger National Park using Sentinel-1 C-Band time series data M Urban, K Heckel, C Berger, P Schratz, IPJ Smit, T Strydom, J Baade, ... koedoe 62 (1), 1-6, 2020 | 16 | 2020 |
| FSelectorRcpp:’Rcpp’implementation of’FSelector’entropy-based feature selection algorithms with a sparse matrix support Z Zawadzki, M Kosinski, K Slomczynski, D Skrzypiec, P Schratz R package version 0.3 8, 2021 | 15 | 2021 |
| mlr: Machine Learning in R., 2013 B Bischl, M Lang, L Kotthoff, J Schiffner, J Richter, Z Jones, G Casalicchio, ... URL https://CRAN. R-project. org/package= mlr. R package version 2, 455, 0 | 14 | |
| mlr3 book M Becker, M Binder, B Bischl, N Foss, L Kotthoff, M Lan, F Pfisterer, ... URl: https://mlr3book. mlr-org. com 28, 29-30, 2021 | 13 | 2021 |
| Mlr3spatiotempcv: Spatiotemporal resampling methods for machine learning in R P Schratz, M Becker, M Lang, A Brenning arXiv preprint arXiv:2110.12674, 2021 | 10 | 2021 |
| Iml: Interpretable machine learning C Molnar, P Schratz R package version 0.5 1, 2018 | 10 | 2018 |
| Monitoring forest health using hyperspectral imagery: Does feature selection improve the performance of machine-learning techniques? P Schratz, J Muenchow, E Iturritxa, J Cortés, B Bischl, A Brenning Remote Sensing 13 (23), 4832, 2021 | 9 | 2021 |
| mlr3spatiotempcv: Spatiotemporal resampling methods for “mlr3” P Schratz, M Becker R Package, 2021 | 9 | 2021 |
| mlr3: Machine learning in R—Next generation M Lang, B Bischl, J Richter, P Schratz, G Casalicchio, S Coors, Q Au, ... | 7 | 2020 |
| Machine learning in R B Bischl, M Lang, L Kotthoff, J Schiffner, J Richter, Z Jones, G Casalicchio, ... R-Package, TU Dortmund, URL http://r-forge. rproject. org/projects/mlr, 2010 | 6 | 2010 |
Alexander BrenningFriedrich Schiller University Jena, Germany在 uni-jena.de 的电子邮件经过验证
