szilard
October 09, 2020
110

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

October 09, 2020

## Transcript

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

7. ...

8. y = f(x
1
,x
2
,...,x
n
)
“Learn” f from data

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

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

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