Data Preparation and the Importance of How Machines Learn

Transcript

2. Data Preparation and the Importance of how Machines Learn Rebecca Vickery, Data Scientist, Holiday Extras
3. Machine learning
4. Source: Google images
5. Simple ML workflow Get data >> baseline model >> model selection >> model tuning >> predict
6. Simple ML workflow Get data >> Features/Inputs What we want to predict
7. Simple ML workflow Baseline model >> Accuracy score Perfect = 1.0 0.44
8. Simple ML workflow Model selection >> Best model = Random Forest
9. Simple ML workflow Hyperparameter optimisation >> Best score = 1.0 Best Params = {'max_depth': 5, 'min_samples_leaf': 1, 'min_samples_split': 10, 'n_estimators': 500}
10. Source: Google images
11. What happens when we have this data set?
12. What happens when we have this data set?
14. Source: thedailybeast.com
15. Actual ML workflow Get data >> data preparation >> feature engineering >> baseline model >> model selection >> model tuning >> predict
16. Label encoding
17. Problem Source: flaticon.com 4 is bigger than 1 so there must be a relationship between these rows Source: flaticon.com 1 = neutered male 2 = spayed female 3 = intact male
18. Solution: One hot encoding
19. Ordinal data
20. Problem: Won’t work for all variables 366 different unique values = 366 new features
21. Solution: Feature engineering? Single Colour Multi Colour
22. Problem: We will lose a lot of information Source: thetelegraph.com
23. Solution: Weight of evidence For each colour (e.g. Tan): WOE = ln ( ( pi /p) / ( ni / n) ) pi = number of times Tan appears in positive class (1) p = total number of positive classes (1) ni = number of times Tan appears in negative class (0) n = total number of negative classes (0)
24. Solution: Weight of evidence Output is a positive or negative number
25. Solution(s) WOE is one of many solutions for this
26. Problem(s) Source: Photo by Louis Reed on Unsplash
27. Solution: Scikit-learn pipelines
28. Solution: category_encoders pip install category_encoders
29. Pipeline example
30. Less time But still some work to do
31. “There are only two Machine Learning approaches that win competitions: Handcrafted & Neural Networks.” Anthony Goldbloom, CEO & Founder, Kaggle
32. Thanks for listening Find me at...