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Achim Zeileis
Researcher at University of Innsbruck
Publications - 331
Citations - 35410
Achim Zeileis is an academic researcher from University of Innsbruck. The author has contributed to research in topics: Regression analysis & Recursive partitioning. The author has an hindex of 63, co-authored 309 publications receiving 29629 citations. Previous affiliations of Achim Zeileis include University of Vienna & VU University Medical Center.
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Unbiased Recursive Partitioning: A Conditional Inference Framework
TL;DR: A unified framework for recursive partitioning is proposed which embeds tree-structured regression models into a well defined theory of conditional inference procedures and it is shown that the predicted accuracy of trees with early stopping is equivalent to the prediction accuracy of pruned trees with unbiased variable selection.
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Bias in random forest variable importance measures: Illustrations, sources and a solution
TL;DR: An alternative implementation of random forests is proposed, that provides unbiased variable selection in the individual classification trees, that can be used reliably for variable selection even in situations where the potential predictor variables vary in their scale of measurement or their number of categories.
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Conditional variable importance for random forests
TL;DR: A new, conditional permutation scheme is developed for the computation of the variable importance measure that reflects the true impact of each predictor variable more reliably than the original marginal approach.
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Regression Models for Count Data in R
TL;DR: In this article, a new implementation of hurdle and zero-inflated regression models in the functions hurdle() and zeroinfl() from the package pscl is introduced, which reuses design and functionality of the basic R functions just as the underlying conceptual tools extend the classical models.
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kernlab - An S4 Package for Kernel Methods in R
TL;DR: The package contains dot product primitives (kernels), implementations of support vector machines and the relevance vector machine, Gaussian processes, a ranking algorithm, kernel PCA, kernel CCA, and a spectral clustering algorithm.