Ensembles of Trees and CLT's: Inference and Machine Learning

Neyman Seminar
Neyman Seminar
Nov 20, 2019 4:00pm to 5:00pm
1011 Evans Hall
Happening As Scheduled
This talk develops methods of statistical inference based around ensembles of decision trees: bagging, random forests, and boosting. Recent results have shown that when the bootstrap procedure in bagging methods is replaced by sub-sampling, predictions from these methods can be analyzed using the theory of U-statistics which have a limiting normal distribution. Moreover, the limiting variance...
Giles Hooker, Cornell University