Machine Learning Conference Covers Credit Risk Prediction
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A hands-on workshop to create your first End-to-End Machine Learning Project, presented by Dr. Mercè Martín, VP of Applications in BigML since 2013. *Machine Learning School for Business Schools 2021: Virtual Conference.
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Machine Learning Conference Covers Credit Risk Prediction
- 1. VIRTUAL CONFERENCE 1st Edition · February 17, 2021 Machine Learning School For Business Schools Organized by: In collaboration with: Powered by: #BigMLSchool @ bigmlcom
- 2. VIRTUAL CONFERENCE 1st Edition · February 17, 2021 Machine Learning School For Business Schools #BigMLSchool @ b i g m l c o m My First End-to-End Machine Learning Project Mercè Martín VP of Applications at
- 3. BigML,Inc #BigMLSchool Do I Need a Model? 3 Well… do you make decisions?
- 5. BigML,Inc #BigMLSchool Credit Delinquency 5 https://www.kaggle.com/c/GiveMeSomeCredit I WANT TO MINIMIZE INVESTMENT RISK BY PREDICTING DEFAULTS
- 7. BigML,Inc #BigMLSchool Defining the Question 7 Is that really all there is to it?…
- 8. BigML,Inc #BigMLSchool Defining the Question 8 Is a customer in default… What’s the maximum delinquency that we allow? Name Month - 3 Month - 2 Month - 1 Joe Schmo 123,23 0 123,23 Jane Plain 0 0 0 Mary Happy 0 246,46 0 Tom Thumb 123,23 123,23 123,23 When he misses some payments? When she pays late?
- 9. BigML,Inc #BigMLSchool Defining the Contest Goal 9 Predicting who will be 90 days past due or worse to act only on them Name Month - 3 Month - 2 Month - 1 Default Joe Schmo 123,23 0 123,23 No Jane Plain 0 0 0 Yes Mary Happy 0 246,46 0 No Tom Thumb 123,23 123,23 123,23 No l a b e l
- 14. BigML,Inc #BigMLSchool The Source 14 How to interpret your data? •Field types •Locale •Missing tokens •Text/Items parsing
- 15. BigML,Inc #BigMLSchool The Dataset 15 How is data distributed? •Histograms •Statistics •Number of missings •Number of errors
- 16. BigML,Inc #BigMLSchool 16 And now… The Model
- 17. BigML,Inc #BigMLSchool The Model 17 What insights will the Model extract? •Patterns •Importance •and…
- 18. BigML,Inc #BigMLSchool The Model 18 What insights will the Model extract? •Predictions (labels) •Confidence •Explanations
- 20. BigML,Inc #BigMLSchool The Evaluation 20 TEST TRAINING EVALUATION MODEL CONFIDENCE PREDICTION % %
- 21. BigML,Inc #BigMLSchool 21 And now… The Evaluation
- 22. BigML,Inc #BigMLSchool The Evaluation 22 Do predictions match the expected values? Hey, great accuracy!!! right?
- 23. BigML,Inc #BigMLSchool The Evaluation 23 I wish to make a complaint!
- 24. BigML,Inc #BigMLSchool The Evaluation 24 Where do errors come from?
- 25. BigML,Inc #BigMLSchool The Costs 25 Where do errors come from? TO MINIMIZE COST WE SHOULD MAXIMIZE THE RECALL Unbalanced •Always remember the goal •And the costs of failing! Predicting who will be 90 days past due or worse
- 26. BigML,Inc #BigMLSchool 26 And now… Model Tuning
- 27. BigML,Inc #BigMLSchool Compensating unbalance 27 When the percentage of examples of the class we are interested in is very low Increasing their frequency can help the model to learn better
- 28. BigML,Inc #BigMLSchool Choosing According to Cost 28 Unbalanced Balanced THE BALANCED MODEL WORKS BETTER
- 31. BigML,Inc #BigMLSchool Automated Tuning 31 Smart search for the best-performing configuration
- 33. BigML,Inc #BigMLSchool And the Winner is… 33 A simple decision tree! •15-nodes •Balanced •Pruned
- 35. BigML,Inc #BigMLSchool How to recognize trees…? 35
- 36. BigML,Inc #BigMLSchool 36 The End No trees were harmed in the making of this workshop
- 37. VIRTUAL CONFERENCE 1st Edition · February 17, 2021 Machine Learning School For Business Schools Organized by: In collaboration with: Powered by: #BigMLSchool @ bigmlcom