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SUMMARY:A Central Limit Theorem for Centered Purely Random Forests using U
-Statistic Theory
DTSTART;VALUE=DATE-TIME:20220803T160000Z
DTEND;VALUE=DATE-TIME:20220803T162000Z
DTSTAMP;VALUE=DATE-TIME:20240914T212633Z
UID:indico-contribution-1220@conference2.aau.at
DESCRIPTION:Random forests are a popular method in supervised learning and
can be\nused for regression and classification problems. For a regression
problem\na random forest averages the results of several randomized decis
ion trees\nthat are constructed on different subsamples of the dataset. In
practice\nrandom forests appear to be very successful and are therefore a
commonly\nused algorithm. Contrary to this there is little known about th
e\nmathematical properties of classic random forests that use data depende
nt\npartitions. Most results in the literature cover simpler versions of r
andom\nforests often with partitions that are independent of the dataset.
One\nexample of these simpler algorithms are centered purely random forest
s.\nMoreover the majority of the results in the literature are consistency
theorems\nand there are noticeably less central limit theorems. In our wo
rk\nwe prove a central limit theorem for centered purely random forests. T
he\nproof uses results by Peng et al. (2022) which are based on an interpr
etation\nof random forests as generalized U-Statistics.\n\n**References**\
nWei Peng\, Tim Coleman\, and Lucas Mentch. Rates of convergence for rando
m\nforests via generalized u-statistics. *Electronic Journal of Statistics
*\,\n16(1):232â€“292\, 2022.\n\nhttps://conference2.aau.at/event/131/contr
ibutions/1220/
LOCATION:UniversitÃ¤t Klagenfurt HS 4
URL:https://conference2.aau.at/event/131/contributions/1220/
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