References in zbMATH (referenced in 109 articles )

Showing results 1 to 20 of 109.
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  1. Bui, Anh Tuan; Apley, Daniel W.: An exploratory analysis approach for understanding variation in stochastic textured surfaces (2019)
  2. Casalicchio, Giuseppe; Bossek, Jakob; Lang, Michel; Kirchhoff, Dominik; Kerschke, Pascal; Hofner, Benjamin; Seibold, Heidi; Vanschoren, Joaquin; Bischl, Bernd: \textttOpenML: an \textttRpackage to connect to the machine learning platform openml (2019)
  3. Lee, Jeong Eun; Nicholls, Geoff K.; Ryder, Robin J.: Calibration procedures for approximate Bayesian credible sets (2019)
  4. Plaia, Antonella; Sciandra, Mariangela: Weighted distance-based trees for ranking data (2019)
  5. Steingrimsson, Jon Arni; Diao, Liqun; Strawderman, Robert L.: Censoring unbiased regression trees and ensembles (2019)
  6. Asfha, Huruy Debessay; Kilinc, Betul Kan: Appraisal of performance of three tree-based classification methods (2018)
  7. Au, Timothy C.: Random forests, decision trees, and categorical predictors: the “absent levels” problem (2018)
  8. Eun-Kyung Lee: PPtreeViz: An R Package for Visualizing Projection Pursuit Classification Trees (2018) not zbMATH
  9. Henckaerts, Roel; Antonio, Katrien; Clijsters, Maxime; Verbelen, Roel: A data driven binning strategy for the construction of insurance tariff classes (2018)
  10. Muñoz, Mario A.; Villanova, Laura; Baatar, Davaatseren; Smith-Miles, Kate: Instance spaces for machine learning classification (2018)
  11. Peter Calhoun; Xiaogang Su;Martha Nunn; Juanjuan Fan: Constructing Multivariate Survival Trees: The MST Package for R (2018) not zbMATH
  12. Quan, Zhiyu; Valdez, Emiliano A.: Predictive analytics of insurance claims using multivariate decision trees (2018)
  13. Valliant, Richard; Dever, Jill A.; Kreuter, Frauke: Practical tools for designing and weighting survey samples (2018)
  14. Zhao, Xin; Barber, Stuart; Taylor, Charles C.; Milan, Zoka: Classification tree methods for panel data using wavelet-transformed time series (2018)
  15. Alvarez-Iglesias, Alberto; Hinde, John; Ferguson, John; Newell, John: An alternative pruning based approach to unbiased recursive partitioning (2017)
  16. Baumer, Benjamin S.; Kaplan, Daniel T.; Horton, Nicholas J.: Modern data science with R (2017)
  17. Berrar, Daniel: Confidence curves: an alternative to null hypothesis significance testing for the comparison of classifiers (2017)
  18. Bertsimas, Dimitris; Dunn, Jack: Optimal classification trees (2017)
  19. Bilton, Penny; Jones, Geoff; Ganesh, Siva; Haslett, Steve: Classification trees for poverty mapping (2017)
  20. Conversano, Claudio; Dusseldorp, Elise: Modeling threshold interaction effects through the logistic classification trunk (2017)

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