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--- seminars:stat:oct172024 [2024/10/10 14:19] – created rakhi
+++ seminars:stat:oct172024 [2024/10/10 14:19] (current) – rakhi
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+<WRAP centeralign>##Statistics Seminar##\\ Department of Mathematics and Statistics</WRAP>
+<WRAP 70% center>
+^  **DATE:**|Thursday, October 17, 2024 |
+^  **TIME:**|1:15pm -- 2:15pm |
+^  **LOCATION:**|WH 100E |
+^  **SPEAKER:**|Samruddhi Thakar, Binghamton University |
+^  **TITLE:**|Quantifying Uncertainty in Random Forests via Confidence Intervals and Hypothesis Tests   |
+</WRAP>
+\\
+<WRAP center box 80%>
+<WRAP centeralign>**Abstract**</WRAP>
+This work develops formal statistical inference procedures for predictions generated by supervised
+learning ensembles. Ensemble methods based on bootstrapping, such as bagging
+and random forests, have improved the predictive accuracy of individual trees, but fail to
+provide a framework in which distributional results can be easily determined. Instead of
+aggregating full bootstrap samples, we consider predicting by averaging over trees built
+on subsamples of the training set and demonstrate that the resulting estimator takes the
+form of a U-statistic. As such, predictions for individual feature vectors are asymptotically
+normal, allowing for confidence intervals to accompany predictions. In practice, a
+subset of subsamples is used for computational speed; here our estimators take the form
+of incomplete U-statistics and equivalent results are derived. We further demonstrate that
+this setup provides a framework for testing the significance of features. Moreover, the internal
+estimation method we develop allows us to estimate the variance parameters and
+perform these inference procedures at no additional computational cost. Simulations and
+illustrations on a real data set are provided.
+Reference:
+Mentch, L., & Hooker, G. (2016). Quantifying uncertainty in random forests via confidence intervals and hypothesis tests. Journal of Machine Learning Research, 17(26), 1-41.
+</WRAP>