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Gradient Boosting Machines (GBM): From Zero to...

szilard
October 09, 2020
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Gradient Boosting Machines (GBM): From Zero to Hero (with R and Python Code) - Data Con LA - Oct 2020

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
  2. 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
  3. ...

  4. 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
  5. 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
  6. Live Demo Summary of the demo for those reading just

    the slides (e.g. those who did not attend the talk):