References in zbMATH (referenced in 29 articles , 1 standard article )

Showing results 1 to 20 of 29.
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  1. Feuerriegel, Stefan; Gordon, Julius: News-based forecasts of macroeconomic indicators: a semantic path model for interpretable predictions (2019)
  2. Lukas W. Lehnert, Hanna Meyer, Wolfgang A. Obermeier, Brenner Silva, Bianca Regeling, Jörg Bendix: Hyperspectral Data Analysis in R: the hsdar Package (2018) arXiv
  3. Benjamin R. Fitzpatrick, Kerrie Mengersen: A network flow approach to visualising the roles of covariates in random forests (2017) arXiv
  4. Conversano, Claudio; Dusseldorp, Elise: Modeling threshold interaction effects through the logistic classification trunk (2017)
  5. Bischl, Bernd; Lang, Michel; Kotthoff, Lars; Schiffner, Julia; Richter, Jakob; Studerus, Erich; Casalicchio, Giuseppe; Jones, Zachary M.: mlr: machine learning in $\bold R$ (2016)
  6. Farzad Noorian and Anthony de Silva and Philip Leong: gramEvol: Grammatical Evolution in R (2016)
  7. Fitzpatrick, Trevor; Mues, Christophe: An empirical comparison of classification algorithms for mortgage default prediction: evidence from a distressed mortgage market (2016)
  8. Teisseyre, Paweł; Kłopotek, Robert A.; Mielniczuk, Jan: Random subspace method for high-dimensional regression with the R package regRSM (2016)
  9. Bertsimas, Dimitris; Brynjolfsson, Erik; Reichman, Shachar; Silberholz, John: OR forum: Tenure analytics: models for predicting research impact (2015)
  10. Fernandez-Lozano, Carlos; Cuiñas, Rubén F.; Seoane, José A.; Fernández-Blanco, Enrique; Dorado, Julian; Munteanu, Cristian R.: Classification of signaling proteins based on molecular star graph descriptors using machine learning models (2015)
  11. Ryan, Kenneth Joseph; Culp, Mark Vere: On semi-supervised linear regression in covariate shift problems (2015)
  12. Arratia, Argimiro: Computational finance. An introductory course with R (2014)
  13. Eugster, Manuel J. A.; Leisch, Friedrich; Strobl, Carolin: (Psycho-)analysis of benchmark experiments: a formal framework for investigating the relationship between data sets and learning algorithms (2014)
  14. Fernández-Delgado, Manuel; Cernadas, Eva; Barro, Senén; Amorim, Dinani: Do we need hundreds of classifiers to solve real world classification problems? (2014)
  15. Martin Sill; Thomas Hielscher; Natalia Becker; Manuela Zucknick: c060: Extended Inference with Lasso and Elastic-Net Regularized Cox and Generalized Linear Models (2014)
  16. Mielniczuk, Jan; Teisseyre, Paweł: Using random subspace method for prediction and variable importance assessment in linear regression (2014)
  17. Shah, Jasmit; Datta, Somnath; Datta, Susmita: A multi-loss super regression learner (MSRL) with application to survival prediction using proteomics (2014)
  18. Stephan Ritter; Nicholas Jewell; Alan Hubbard: R Package multiPIM: A Causal Inference Approach to Variable Importance Analysis (2014)
  19. Weihs, Claus; Mersmann, Olaf; Ligges, Uwe: Foundations of statistical algorithms. With references to R packages (2014)
  20. Esteban Alfaro; Matias Gamez; Noelia García: adabag: An R Package for Classification with Boosting and Bagging (2013)

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