Big Data and Predictive Modeling

Transcript

1. Big Data and Predictive Modeling Olivier Grisel — @ogrisel Web

   We Can March 21, 2015

2. About me

3. Big Data as a buzzword

4. Triumph of the Nerds: Nate Silver Wins in 50 States

   http://mashable.com/2012/11/07/nate-silver-wins/

5. Triumph of the Nerds: Nate Silver Wins in 50 States

   http://mashable.com/2012/11/07/nate-silver-wins/

6. Nate Silver’s election model, Big Data? $ git clone gh:jseabold/538model

   $ du -h 538model/data 188K 538model/data

7. 15% of the capacity of a 3’5 floppy disk

8. Regionator 3000 http://labs.data-publica.com/regionator3000/

9. http://transports.blog.lemonde.fr/2014/06/05/regionator-la- carte-de-france-dessinee-par-les-trajets-quotidiens/

10. http://transports.blog.lemonde.fr/2014/06/05/regionator-la- carte-de-france-dessinee-par-les-trajets-quotidiens/

11. http://www.insee.fr/fr/themes/detail.asp? reg_id=99&ref_id=mobilite-professionnelle-10

12. http://www.insee.fr/fr/themes/detail.asp? reg_id=99&ref_id=mobilite-professionnelle-10

13. 120% of the capacity of a 3’5 floppy disk

14. Big Data ≠ Predictive Analytics

15. Predictive Analytics ≠ Descriptive Analytics

16. Goals of this talk • What Big Data actually is

    or isn’t • Introduce predictive modeling concepts • Contrast predictive analytics vs descriptive analytics

17. How big is Big Data?

18. – Wikipedia “Big data is a blanket term for any

    collection of data sets so large and complex that it becomes difficult to process using on-hand database management tools or traditional data processing applications.”

19. Not Big Data • Data that fits on a spreadsheet

    • Data that can be analyzed in RAM (< 10 GB) • Data operations that can be performed quickly by a traditional database, e.g. single node PostgreSQL server

20. Reading the full content of a 1TB HDD at 100MB/s:

    2 hours 45 minutes

21. Canonical Big Data problem: indexing the Web • Inverted index

    on tera bytes of text data • Process each HTML page as a URL + bag of words • For each word, aggregate the list of page URLs • 2 billion HTML pages: 100TB >10 days just to read sequentially
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25. Other Big Data examples • GSM location event log from

    telco • Transaction log of a big retail network • Raw traffic data on a large website • Activity records for a service with 10s of millions of users

26. Not Big Data • Polls data (~10K data points) •

    Census data (~10M data points) • Real estate transactions data (~10M data points) • Any dataset publicly available for download

27. What is Predictive Analytics? and Machine Learning

28. Type # rooms Surface m2 Floor Public Transports Apartment 3

    65 2 Yes House 5 110 NA No Duplex 4 95 4 Yes Sold 300k 1.5M 2.2M

29. Type # rooms Surface m2 Floor Public Transports Apartment 3

    65 2 Yes House 5 110 NA No Duplex 4 95 4 Yes features samples Sold 300k 1.5M 2.2M target

30. Type # rooms Surface m2 Floor Public Transports Apartment 3

    65 2 Yes House 5 110 NA No Duplex 4 95 4 Yes features samples Sold 300k 1.5M 2.2M target Apartment 2 35 3 Yes ?

31. Predictive Modeling • Automated predictions of outcome on new data

    • Alternative to hard-coded rules written by experts • Extract the structure of historical data • Statistical tools to summarize the training data into an executable predictive model

32. Training text docs images sounds transactions Labels Machine Learning Algorithm

    Model Predictive Modeling Data Flow Feature vectors

33. New text doc image sound transaction Model Expected Label Predictive

    Modeling Data Flow Feature vector Training text docs images sounds transactions Labels Machine Learning Algorithm Feature vectors

34. Descriptive vs Predictive

35. Descriptive Statistics • Ex: Sales by (day, months, year) x

    region • Graphical visualization: get insights, tell a story to explain what’s happening in the data • Realm of Business Intelligence: reports & dashboard for managers • A wrong decision can be very costly • Small number of important decisions made by a human

36. Predictive Statistics • Ex: Movie recommendations or targeted ads •

    Embedded in a user service to make it more useful / attractive / profitable • A wrong individual decision is not costly • Large number of small automated decisions • Humans would not be fast enough to make the predictions

37. Mixed models • Predictive modeling to identify interesting subsets of

    the data • Ex: Fraud detection, churn forecasting • Help human decision makers focus on important cases • Human expert feedback to improve predictive models

38. Key takeaway points:

39. • Big Data ≠ Predictive Analytics • Predictive Analytics •

    Automated decision making embedded in products (e.g. recommenders) • Individual bad decisions are typically not costly • Descriptive Analytics • Business Intelligence: human decision making • Individual bad decisions can be very costly

40. Thank you! Questions? @Inria @ogrisel http://scikit-learn.org

41. Bonus tracks

42. Back to the Regionator What if we did not have

    census data on daily mobility?

43. Back to the Regionator • Use raw daily telco logs

    • Group By (phone, day) to extract daily trips • Join By GPS coordinates to “departement” names • Filter out small trips • Group By (home, work) “departements” • Count

44. Tools for predictive analytics

45. SPSS MATLAB

46. SPSS MATLAB

47. New text doc image sound transaction Model Expected Label Small

    data Training text docs images sounds transactions Labels Machine Learning Algorithm Feature vectors Feature vector

48. New text doc image sound transaction Model Expected Label Small

    / Medium data Training text docs images sounds transactions Labels Machine Learning Algorithm Feature vectors Feature vector

49. New text doc image sound transaction Model Expected Label Small

    / Medium data with Training text docs images sounds transactions Labels Machine Learning Algorithm Feature vectors Feature vector

50. New text doc image sound transaction Model Expected Label Small

    / Medium data with Training text docs images sounds transactions Labels Machine Learning Algorithm Feature vectors Feature vector
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53. Predictive Analytics on Big Data

54. Model Expected Label Big data with Machine Learning Algorithm New

    text doc image sound transaction Training text docs images sounds transactions Labels Feature vectors Feature vector

55. Model Expected Label Big data with Machine Learning Algorithm New

    text doc image sound transaction Training text docs images sounds transactions Labels Feature vectors Feature vector

56. Model Expected Label Big data with Machine Learning Algorithm New

    text doc image sound transaction Training text docs images sounds transactions Labels Feature vectors Feature vector
57. None

58. BIG DATA

59. BIG DATA small(er) data

60. BIG DATA small(er) data

61. From Big to Small • Feature extraction often shrinks data

    • Filter / Join / Group By / Count • Machine Learning performed on aggregates • Sampling for fast in-memory iterative modeling

62. Data size and modeling quality

63. – Peter Norvig, Research Director, Google “We don’t have better

    algorithms. We just have more data.”
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67. More data beats better models?

68. http://technocalifornia.blogspot.fr/2012/07/more-data-or-better- models.html

69. Let’s train a parametric model to read handwritten digits from

    gray level pixels.
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71. model stops improving

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73. Bias vs Variance

74. high bias

75. high bias high variance

76. high bias high variance low variance

77. Variance solution #1: collect more samples

78. Let’s train a non-parametric model to read handwritten digits from

    gray level pixels.
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80. high variance almost no bias variance decreasing with #samples

81. Bias solution #1: non-parametric models

82. Type # rooms Surface (m2) Floor Public Transp. Apart. 3

    65 2 Yes House 5 110 NA No Duplex 4 95 4 Yes features samples

83. Type # rooms Surface (m2) Floor Public Transp. School (km)

    Flood plain Apart. 3 65 2 Yes 1.0 No House 5 110 NA No 25.0 Yes Duplex 4 95 4 Yes 0.5 No features samples

84. Bias solution #2: richer features

85. Data has 2 dimensions: # samples and # features

86. None

87. • Parametric e.g. linear model (traditional stats) vs Non-parametric e.g.

    Random Forests, Neural Networks (Machine Learning) • Understand a model with 10% accuracy vs blindly trust a model with 90% accuracy • Simple models e.g. F = m a, F = - G (m1 + m2) / r^2 will not become false(r) because of big data • New problems can be tackled: computer vision, speech recognition, natural language understanding

88. • the (experimental) scientific method introduced by Karl Popper is

    based on the falsifiability of formulated hypotheses • theory is correct as long as past predictions hold in new experiments • machine learning train-validation-test splits and cross-validation is similar in spirit • ml model is just a complex theory: correct as long as its predictions still hold