Gradient Boosting Machines (GBM): From Zero to Hero (with R and Python Code) - Data Con LA - Oct 2020

Ce8e94cc306ba164175f693fb01aa8b0?s=47 szilard
October 09, 2020
11

Gradient Boosting Machines (GBM): From Zero to Hero (with R and Python Code) - Data Con LA - Oct 2020

Ce8e94cc306ba164175f693fb01aa8b0?s=128

szilard

October 09, 2020
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  1. Gradient Boosting Machines (GBM): From Zero to Hero (with R

    and Python Code) Szilard Pafka, PhD Chief Scientist, Epoch Data Con LA (Online) Oct 2020
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  3. 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
  4. Source: Andrew Ng

  5. Source: Andrew Ng

  6. Source: Andrew Ng

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  12. Source: https://twitter.com/iamdevloper/

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  15. ...

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  22. y = f(x 1 ,x 2 ,...,x n ) “Learn”

    f from data
  23. y = f(x 1 ,x 2 ,...,x n )

  24. y = f(x 1 ,x 2 ,...,x n )

  25. Supervised Learning Data: X (n obs, p features), y (labels)

    Regression, classification Train/learn/fit f from data (model) Score: for new x, get f(x) Algos: LR, k-NN, DT, RF, GBM, NN/DL, SVM, NB… Goal: max acc/min err new data Metrics: MSE, AUC (ROC) Bad: measure on train set. Need: test set/cross-validation (CV) Hyperparameters, model capacity, overfitting Regularization Model selection Hyperparameter search (grid, random) Ensembles
  26. Supervised Learning Data: X (n obs, p features), y (labels)

    Regression, classification Train/learn/fit f from data (model) Score: for new x, get f(x) Algos: LR, k-NN, DT, RF, GBM, NN/DL, SVM, NB… Goal: max acc/min err new data Metrics: MSE, AUC (ROC) Bad: measure on train set. Need: test set/cross-validation (CV) Hyperparameters, model capacity, overfitting Regularization Model selection Hyperparameter search (grid, random) Ensembles
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  29. Source: Hastie etal, ESL 2ed

  30. Source: Hastie etal, ESL 2ed

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  50. no-one is using this crap

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  54. Live Demo Summary of the demo for those reading just

    the slides (e.g. those who did not attend the talk):
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  68. http://www.jmlr.org/papers/volume13/bergstra12a/bergstra12a.pdf

  69. End of Demo

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