Event Date: December 3, 2020 16:15
Significant Feature Selection with Random Forest
Abstract:
The Random Forest method as a tree-based regression and classification algorithm is able to produce feature selection methods along with point predictions during tree construction. Regarding theoretical results, it has been proven that the Random Forest method is consistent, but several other results are still lacking due to the complex mathematical forces involved in this algorithm. Focusing on the Random Forest as a feature selection method, we will deliver theoretical guarantees for the unbiasedness and consistency of the permutation importance measure used during regression tree construction. The result is important to conduct later statistical inference in terms of obtaining (asymptotically) valid statistical testing procedures. Regarding the latter, a brief overview of obtained results will be given and various approaches for future research in this field will be presented. Our results will be supported by extensive simulation experiments.
Short bio:
Burim Ramosaj is Post-Doctoral Researcher at the Faculty of Statistics at TU Dortmund University, where he graduated as Dr. rer. nat. In July 2020 with the Dissertation "Analyzing Consistency and Statistical Inference in Random Forest Models“. From April 2019 he worked there as a Research Assistant and Doctoral Student at TU Dortmund and before that at the Institute of Statistics, University of Ulm. He received a M.Sc. of Mathematics at Syracuse University, NY, USA and a M.Sc. of Mathematics and Management at the University of Ulm. His current research interests are: Asymptotic and Non-Parametric Statistics, Non-Parametric Classification and Regression, Statistical Inference with Machine Learning Methods, Missing Value Imputation.
