ML LAId bare. Cambridge Wireless SIG Meeting. Mary-Ann & Phil Claridge 23 November

Transcription

1 ML LAId bare Cambridge Wireless SIG Meeting Mary-Ann & Phil Claridge 23 November

2 Welcome To Our Toolbox Our Opinionated Views! Data IDE Wrangling Mainline Exploration and Prototyping For Programmers Supporting Cast Up and Coming Hard Thinking Datasets & Kaggle Demos 2

3 Data IDE H2O Weka Honorable mention: BigML (free to use, not open source) 3

4 H20 Good Good analysis of performance of generated AI/ML. Some ML knowledge required to interpret terminology and results. Easy install (local or cloud) Dowload, unzip java jar h2o.jar Sparking Water provides integration with Spark for large Data Sets. Bad Model generation focused on Java Recommended Excellent results for many commercial projects from open source. Demo later! 4

5 Installing H20 5

6 Weka Good Lots of basic data wrangling with no code. E.g String to vector. High degree of control to evaluate different ML algorithms Auto Weka Bad Now a little dated Focus on traditional Machine Learning Recommended Learn how ML works, and ML algorithms without coding. Some quick and dirty wrangling. Good set of free training videos focus on ML not programming. 6

7 Aside: BigML (not open source) Good Point and click model building Download ready to use models in most languages including Python, C#, Java, Javascript and Excel! Decision tree (inc Ensemble) + Neural Nets Fantastic graphics. Bad Not open source (but free to use for small data sets). Recommended Three days pay for three hours work For many commercial applications. Explaining basic ML to non technical audience. White box vs black box. Sufficient for poor quality or low volume data where more sophisticated tools no benefit 7

8 Wrangling Small: Python + Pandas Large: Python + Spark Large, complex and production: Scala + Spark Honorable supporting cast: Anaconda: Pre-built Python environment Parquet: Database table as a file. Fast & small Intellij: Commercial IDE Python - Java, Scala, Javascript, Python Web 8

9 Programming Basic ML + Neural Nets Python and Scikit Tensorflow (+ Keras) Scala Spark.ML 9

10 AI Gym Reinforcement Learn Reinforcement learning Go play. A very different kind of AI No demo today! 10

11 Demo Data - Pima Indians Diabetes Data Set 1. Number of times pregnant 2. Plasma glucose concentration a 2 hours in an oral glucose tolerance test 3. Diastolic blood pressure (mm Hg) 4. Triceps skin fold thickness (mm) 5. 2-Hour serum insulin (mu U/ml) 6. Body mass index (weight in kg/(height in m)^2) 7. Diabetes pedigree function 8. Age (years) 9. Class variable (0 or 1) For more data sets: Kaggle! 11

12 Model Accuracy Tool Model Accuracy H20 Ten level tree 97% H20 Neural Net 97.5% BigML 48 Node Decision Tree 82.7% BigML Neural Net (shallow) 77% Keras 3 Layer Simple Neural Net 77.73% Keras Deeper neural net To follow 12

13 Next Demos H20 Keras Q&A 13

14 Screen Shots Demo H2O - import PIMA Indian diabetes data set, and build decision tree 14

15 H2O 15

16 H2O 16

17 H2O 17

18 H2O 18

19 H2O 19

20 H2O 20

21 H2O 21

22 H2O 22

23 H2O 23

24 BigML same process as H2O demo above 24

25 BigML 25

26 BigML 26

27 BigML 27

28 BigML 28

29 BigML 29

30 BigML 30

31 BigML 31

32 BigML 32

33 BigML 33

34 BigML 34

35 BigML 35

36 BigML 36

37 BigML 37