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GBM Workshop - Budapest Data Forum Conference -...

szilard
June 09, 2018
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GBM Workshop - Budapest Data Forum Conference - June 2018

szilard

June 09, 2018
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  1. Better than Deep Learning: Gradient Boosting Machines (GBM) Szilárd Pafka,

    PhD Chief Scientist, Epoch (USA) ½ Day Workshop, Budapest Data Forum Conference June 2018
  2. At a Glance... ML: sup.L: y = f(x) “learn” f

    from data (y, X) training, testing/prediction, algos (LR,DT,NN…), optimization, overfitting, regularization... GBM: ensemble of decision trees GBM libs: R/Python
  3. Schedule: 1. Intro talk (slides) 2. Demo main features (me

    running code) 3. Hands-on (you install/run code)
  4. 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
  5. ...

  6. structured/tabular data: GBM (or RF) very small data: LR very

    large sparse data: LR with SGD (+L1/L2) images/videos, speech: DL
  7. structured/tabular data: GBM (or RF) very small data: LR very

    large sparse data: LR with SGD (+L1/L2) images/videos, speech: DL it depends
  8. structured/tabular data: GBM (or RF) very small data: LR very

    large sparse data: LR with SGD (+L1/L2) images/videos, speech: DL it depends / try them all
  9. structured/tabular data: GBM (or RF) very small data: LR very

    large sparse data: LR with SGD (+L1/L2) images/videos, speech: DL it depends / try them all / hyperparam tuning
  10. structured/tabular data: GBM (or RF) very small data: LR very

    large sparse data: LR with SGD (+L1/L2) images/videos, speech: DL it depends / try them all / hyperparam tuning / ensembles
  11. structured/tabular data: GBM (or RF) very small data: LR very

    large sparse data: LR with SGD (+L1/L2) images/videos, speech: DL it depends / try them all / hyperparam tuning / ensembles feature engineering
  12. structured/tabular data: GBM (or RF) very small data: LR very

    large sparse data: LR with SGD (+L1/L2) images/videos, speech: DL it depends / try them all / hyperparam tuning / ensembles feature engineering / other goals e.g. interpretability
  13. structured/tabular data: GBM (or RF) very small data: LR very

    large sparse data: LR with SGD (+L1/L2) images/videos, speech: DL it depends / try them all / hyperparam tuning / ensembles feature engineering / other goals e.g. interpretability the title of this talk was misguided
  14. structured/tabular data: GBM (or RF) very small data: LR very

    large sparse data: LR with SGD (+L1/L2) images/videos, speech: DL it depends / try them all / hyperparam tuning / ensembles feature engineering / other goals e.g. interpretability the title of this talk was misguided but so is recently almost every use of the term AI
  15. I usually use other people’s code [...] I can find

    open source code for what I want to do, and my time is much better spent doing research and feature engineering -- Owen Zhang
  16. 10x

  17. 10x

  18. “people that know what they’re doing just use open source

    [...] the same open source tools that the MLaaS services offer” - Bradford Cross ML training: lots of CPU cores lots of RAM limited time