Gradient Boosting Machines (GBM):
From Zero to Hero (with R and Python Code)
Szilard Pafka, PhD
Chief Scientist, Epoch
LA Data Science Meetup
Febr 2020
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
Source: https://twitter.com/iamdevloper/
...
y = f(x1,x2,...,xn)
“Learn” f from data
y = f(x1,x2,...,xn)
y = f(x1,x2,...,xn)
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
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
Source: Hastie etal, ESL 2ed
Source: Hastie etal, ESL 2ed
no-one is using
this crap
Live Demo
Summary of the demo for those reading just the slides (e.g. those who did not attend the talk):
http://www.jmlr.org/papers/volume13/bergstra12a/bergstra12a.pdf
End of Demo