References in zbMATH (referenced in 22 articles )

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  1. Boehmke, Brad; Greenwell, Brandon M.: Hands-on machine learning with R (2020)
  2. Lai, Yuanhao; McLeod, Ian: Ensemble quantile classifier (2020)
  3. Liu, Nan-Ting; Lin, Feng-Chang; Shih, Yu-Shan: Count regression trees (2020)
  4. Mozharovskiĭ, I. S.; Samotylova, S. A.; Torgashov, A. Yu.: Predictive modeling of mass-transfer technological plant using an algorithm of alternating conditional expectations (2020)
  5. Sambasivan, Rajiv; Das, Sourish; Sahu, Sujit K.: A Bayesian perspective of statistical machine learning for big data (2020)
  6. García Nieto, P. J.; García-Gonzalo, E.; Sánchez Lasheras, F.; Paredes-Sánchez, J. P.; Riesgo Fernández, P.: Forecast of the higher heating value in biomass torrefaction by means of machine learning techniques (2019)
  7. Haq, Anam; Wilk, Szymon; Abelló, Alberto: Fusion of clinical data: a case study to predict the type of treatment of bone fractures (2019)
  8. Thao, Le Thi Phuong; Geskus, Ronald: A comparison of model selection methods for prediction in the presence of multiply imputed data (2019)
  9. Wang, Wei; Lin, Nan; Tang, Xiang: Robust two-sample test of high-dimensional mean vectors under dependence (2019)
  10. Bokhari, Ehsan; Hubert, Lawrence: The lack of cross-validation can lead to inflated results and spurious conclusions: a re-analysis of the MacArthur violence risk assessment study (2018)
  11. Denuit, Michel; Legrand, Catherine: Risk classification in life and health insurance: extension to continuous covariates (2018)
  12. Henckaerts, Roel; Antonio, Katrien; Clijsters, Maxime; Verbelen, Roel: A data driven binning strategy for the construction of insurance tariff classes (2018)
  13. Liu, Han; Cocea, Mihaela: Induction of classification rules by Gini-index based rule generation (2018)
  14. Lukas W. Lehnert, Hanna Meyer, Wolfgang A. Obermeier, Brenner Silva, Bianca Regeling, Jörg Bendix: Hyperspectral Data Analysis in R: the hsdar Package (2018) arXiv
  15. Mirylenka, Katsiaryna; Giannakopoulos, George; Do, Le Minh; Palpanas, Themis: On classifier behavior in the presence of mislabeling noise (2017)
  16. Povalej Bržan, P.; Gallego, J. A.; Romero, J. P.; Glaser, V.; Rocon, E.; Benito-León, J.; Bermejo-Pareja, F.; Posada, I. J.; Holobar, A.: New perspectives for computer-aided discrimination of Parkinson’s disease and essential tremor (2017)
  17. Tyralis, Hristos; Papacharalampous, Georgia: Variable selection in time series forecasting using random forests (2017)
  18. Adhikari, Prem Raj; Vavpetič, Anže; Kralj, Jan; Lavrač, Nada; Hollmén, Jaakko: Explaining mixture models through semantic pattern mining and banded matrix visualization (2016)
  19. Biau, Gérard; Scornet, Erwan: A random forest guided tour (2016)
  20. Blaser, Rico; Fryzlewicz, Piotr: Random rotation ensembles (2016)

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