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Better than My Meetup/Conference Talks: Going Deeper in Various GBM Topics - GBM Advanced Workshop - Budapest, Nov 2019
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szilard
November 09, 2019
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Better than My Meetup/Conference Talks: Going Deeper in Various GBM Topics - GBM Advanced Workshop - Budapest, Nov 2019
szilard
November 09, 2019
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Transcript
Better than My Meetup/Conference Talks: Going Deeper in Various GBM
Topics Szilard Pafka, PhD Chief Scientist, Epoch (USA) GBM Advanced Workshop Budapest Nov 2019
Why GBMs
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meetup/conference talks going deeper section dividers
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Disclaimer: I am not representing my employer (Epoch) in this
talk I cannot confirm nor deny if Epoch is using any of the methods, tools, results etc. mentioned in this talk
Source: Andrew Ng
Source: Andrew Ng
Source: Andrew Ng
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http://lowrank.net/nikos/pubs/empirical.pdf http://www.cs.cornell.edu/~alexn/papers/empirical.icml06.pdf
http://lowrank.net/nikos/pubs/empirical.pdf http://www.cs.cornell.edu/~alexn/papers/empirical.icml06.pdf
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top algos (RF, boosting), all features 2007
top algos (RF, boosting), all features most algos (lin, tree,
nnet) worst algos (knn, NB) 2007
top algos (RF, boosting), all features most algos (lin, tree,
nnet) worst algos (knn, NB) top algos, removed top feature(s) 2007
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Source: Hastie etal, ESL 2ed
Source: Hastie etal, ESL 2ed
GBM libs
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10x
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Scoring
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* very first request not shown >500ms (JVM “warmup”)
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GBM-perf github repo
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multi-core/socket
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CPU 1
CPU 1 CPU 2
CPU 1 CPU 2
CPU 1 CPU 2
CPU 1 CPU 2
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5x 3.5x
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zero
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Spark
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GPU
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catboost
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API / tuning
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http://www.jmlr.org/papers/volume13/bergstra12a/bergstra12a.pdf
http://www.argmin.net/2016/06/20/hypertuning/
http://www.argmin.net/2016/06/20/hypertuning/
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time ordered data time ordered data
time ordered data time ordered data train sample
time ordered data time ordered data train test sample sample
(slightly different distribution)
time ordered data time ordered data train test sample sample
proper train early stopping Model selection resampled 80-10-10 (~CV) (slightly different distribution)
time ordered data time ordered data train test sample sample
proper train early stopping Model selection random search over lightgbm resampled 80-10-10 (~CV) (slightly different distribution)
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Closing
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Source: https://www.linkedin.com/pulse/winning-solution-kaggledays-2019-competition-san-francisco-mark-peng/
Source: https://www.linkedin.com/pulse/winning-solution-kaggledays-2019-competition-san-francisco-mark-peng/
Source: https://www.linkedin.com/pulse/winning-solution-kaggledays-2019-competition-san-francisco-mark-peng/
Source: https://www.linkedin.com/pulse/winning-solution-kaggledays-2019-competition-san-francisco-mark-peng/
Source: https://www.linkedin.com/pulse/winning-solution-kaggledays-2019-competition-san-francisco-mark-peng/
Source: https://www.linkedin.com/pulse/winning-solution-kaggledays-2019-competition-san-francisco-mark-peng/
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