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.
Source: Andrew Ng

4.
Source: Andrew Ng

5.
Source: Andrew Ng

6.
Source: https://twitter.com/iamdevloper/

7.
...

8.
y = f(x1
,x2
,...,xn
)
“Learn” f from data

9.
y = f(x1
,x2
,...,xn
)

10.
y = f(x1
,x2
,...,xn
)

11.
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

12.
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

13.
Source: Hastie etal, ESL 2ed

14.
Source: Hastie etal, ESL 2ed

15.
no-one is using
this crap

16.
Live Demo
Summary of the demo for those reading just the
slides (e.g. those who did not attend the talk):

17.
http://www.jmlr.org/papers/volume13/bergstra12a/bergstra12a.pdf

18.
End of Demo