Upgrade to Pro
— share decks privately, control downloads, hide ads and more …
Speaker Deck
Features
Speaker Deck
PRO
Sign in
Sign up for free
Search
Search
Better than My Meetup/Conference Talks: Going ...
Search
szilard
November 09, 2019
0
54
Better than My Meetup/Conference Talks: Going Deeper in Various GBM Topics - GBM Advanced Workshop - Budapest, Nov 2019
szilard
November 09, 2019
Tweet
Share
More Decks by szilard
See All by szilard
Gradient Boosting Machines (GBM): From Zero to Hero (with R and Python Code) - Data Con LA - Oct 2020
szilard
0
140
Make Machine Learning Boring Again: Best Practices for Using Machine Learning in Businesses - Albuquerque Machine Learning Meetup (Online) - Aug 2020
szilard
0
94
Better than Deep Learning: Gradient Boosting Machines (GBM) - eRum conference - invited talk - June 2020
szilard
0
92
Gradient Boosting Machines (GBM): From Zero to Hero (with R and Python Code) - LA Data Science Meetup - February 2020
szilard
0
83
A Random Walk in Data Science and Machine Learning in Practice - CEU, Business Analytics Masters - Budapest, Febr 2020
szilard
0
290
Gradient Boosting Machines (GBM): From Zero to Hero (with R and Python Code) - Budapest BI Forum, Budapest, Nov 2019
szilard
0
130
Make Machine Learning Boring Again: Best Practices for Using Machine Learning in Businesses - LA Data Science Meetup - Playa Vista, August 2019
szilard
0
100
Better than Deep Learning: Gradient Boosting Machines (GBM) / 2019 edition - Budapest R and Data Science Meetups - Budapest, June 2019
szilard
0
80
Better than Deep Learning: Gradient Boosting Machines (GBM) / 2019 edition - LA R Meetup - Santa Monica, May 2019
szilard
0
20
Featured
See All Featured
Designing Dashboards & Data Visualisations in Web Apps
destraynor
229
52k
Designing Experiences People Love
moore
138
23k
Build The Right Thing And Hit Your Dates
maggiecrowley
33
2.4k
JavaScript: Past, Present, and Future - NDC Porto 2020
reverentgeek
47
5k
The Myth of the Modular Monolith - Day 2 Keynote - Rails World 2024
eileencodes
16
2.1k
Cheating the UX When There Is Nothing More to Optimize - PixelPioneers
stephaniewalter
280
13k
Code Reviewing Like a Champion
maltzj
520
39k
We Have a Design System, Now What?
morganepeng
50
7.2k
YesSQL, Process and Tooling at Scale
rocio
169
14k
Why Our Code Smells
bkeepers
PRO
334
57k
Designing for humans not robots
tammielis
250
25k
jQuery: Nuts, Bolts and Bling
dougneiner
61
7.5k
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
None
meetup/conference talks going deeper section dividers
None
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
None
None
None
None
None
None
...
None
None
None
None
None
None
None
None
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
None
None
None
None
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
None
None
None
Source: Hastie etal, ESL 2ed
Source: Hastie etal, ESL 2ed
GBM libs
None
None
None
None
10x
10x
None
None
None
None
None
None
None
None
None
None
None
None
None
None
None
None
None
None
None
None
None
Scoring
None
None
None
None
None
* very first request not shown >500ms (JVM “warmup”)
None
None
None
None
None
None
None
None
None
None
GBM-perf github repo
None
None
None
None
None
None
None
multi-core/socket
None
None
None
None
None
None
CPU 1
CPU 1 CPU 2
CPU 1 CPU 2
CPU 1 CPU 2
CPU 1 CPU 2
None
5x 3.5x
None
None
None
None
None
None
None
None
None
None
None
None
None
None
zero
None
None
Spark
None
None
None
None
None
None
None
None
None
None
None
None
None
None
None
None
None
None
None
None
None
None
None
None
None
None
None
None
GPU
None
None
None
None
catboost
None
None
None
None
None
None
None
None
None
None
API / tuning
None
None
None
None
None
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/
None
None
None
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)
None
None
None
None
None
None
None
None
Closing
None
None
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/
None
More:
None