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DutchMLSchool. ML Business Perspective

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Machine Learning: Business Perspective - Main Conference: Introduction to Machine Learning.
DutchMLSchool: 1st edition of the Machine Learning Summer School in The Netherlands.

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DutchMLSchool. ML Business Perspective

  1. 1. 1st edition | July 8-11, 2019
  2. 2. BigML, Inc #DutchMLSchool 2 ML: Business Perspective A Gentle Introduction to Machine Learning Charles Parker VP, Machine Learning Algorithms
  3. 3. BigML, Inc #DutchMLSchool 3 In This Talk • A simple introduction to supervised machine learning • An introduction to some of the core concepts of the BigML platform • A tiny peek behind the curtain to see what really happens when ML algorithms learn a model • Ways to evaluate and interpret your model’s predictions
  4. 4. BigML, Inc #DutchMLSchool 4 A Churn Problem • You are the CEO of a mobile phone company (congratulations!) • Some percentage of your customers leave the service (or “churn” every month) • You have a budget to reach out to some customers each month to try to persuade them to stay with the service (with, for example, incentives) • But to do that, you need to find out who those customers are
  5. 5. BigML, Inc #DutchMLSchool 5 Begin with the End In Mind • Currently, you have a simple targeting strategy designed by hand that identifies the 10,000 most likely customers to churn • For every five people you call, two are actually thinking about leaving (4,000 for a precision of 40%) • Of these customers, your operators can convince half to stay (so about 2000) • Each of these saved customers has a net value of $500 • What if you could increase the precision of your targeting to 50%?
  6. 6. BigML, Inc #DutchMLSchool 6 You Have The Data! Minutes Used Last Month’s Bill Calls To Support Website Visits Churn? 104 103,60 0 0 No 124 56,33 1 0 No 56 214,60 2 0 Yes 2410 305,60 0 5 No 536 145,70 0 0 No 234 122,09 0 1 No 201 185,76 1 7 Yes 111 83,60 3 2 No
  7. 7. BigML, Inc #DutchMLSchool 7 Now . . . Magic! • Can we use this data to create a better targeting strategy? (Spoiler: Yes!) • Can we use the very same data to measure the effectiveness of that strategy? (Spoiler: Yes!) • And how do we do that? (Spoiler: MACHINE LEARNING)
  8. 8. BigML, Inc #DutchMLSchool 8 Aside: BigML Resources • We can now upload that data to BigML • Everything created on BigML is a resource • Resources are: • Mostly immutable: You can’t “screw them up” • Assigned a unique ID • Always available via both the API and the UI • Working with BigML is a process of creating resources
  9. 9. BigML, Inc #DutchMLSchool 9 Data Sources @ BigML • A data source is a raw data file that you upload to the BigML platform • We make some initial guesses about the number and type of columns in the file, and a bit about their content (such as the language for text fields) • Data can come from uploaded CSVs, Google drive, dropbox, a random URL, and so on
  10. 10. BigML, Inc #DutchMLSchool 10 Datasets @ BigML • A BigML dataset represents processed row-column data • We’ve made a final determination of the number and type of columns in the source • Some summary stats have been calculated for each column
  11. 11. BigML, Inc #DutchMLSchool 11 Supervised Machine Learning • Collect training data from the past about your prediction problem, including the right answer (e.g., statistics for each customer month and whether or not the customer churned at the end of that month) • Feed that data to a machine learning algorithm • The algorithm creates a program (that we typically call a model, or classifier or predictor) which can make that prediction for you on future data
  12. 12. BigML, Inc #DutchMLSchool 12 Traditional: Expert and Programmer • Machine learning breaks the expert system paradigm • To make an expert software system before machine learning, you used an expert and a programmer • The expert’s job was to know how the system should work and be able to communicate that knowledge • The programmer’s job was to convert the expert’s knowledge into a running computer program • These could be the same person, but you must have both of them
  13. 13. BigML, Inc #DutchMLSchool 13 Now: Data and Algorithm • Instead of an expert we have data • Data can be easier to get (and is in some cases already there) • You can get a volume of data much larger than any expert could possibly see • Humans are notoriously bad at being good at things: • https://www.newscientist.com/article/mg21628930-400-specialist-knowledge-is-useless-and-unhelpful • Instead of a programmer we have a learning algorithm • Once you have the data in the proper format, learning algorithms work much faster (enabling iteration) • Learning algorithms are modular
  14. 14. BigML, Inc #DutchMLSchool 14 Back to the Data Minutes Used Last Month’s Bill Calls To Support Website Visits Churn? 104 103,60 0 0 No 124 56,33 1 0 No 56 214,60 2 0 Yes 2410 305,60 0 5 No 536 145,70 0 0 No 234 122,09 0 1 No 201 185,76 1 7 Yes 111 83,60 3 2 No
  15. 15. BigML, Inc #DutchMLSchool 15 The Goal: A Program that Predicts • The goal of learning is to take this sort of training data and create a program (a model or classifier or predictor) • This model takes as input a single row with a value for each of the columns given in the training data • The model will output its predicted value for the objective based on the given column values • Importantly this row can contain any values for the given columns, not just the ones seen in the training data
  16. 16. BigML, Inc #DutchMLSchool 16 Just a Little Peek Under the Curtain
  17. 17. BigML, Inc #DutchMLSchool 17 Behind The Scenes • A learning algorithm is: • A space of models that can be learned (a hypothesis space) • A clever way of searching through that space to find a “good” model • A good model is one that, for example, makes accurate predictions on the training data • So “machine learning” is finding a model amongst all possible models that has a good “fit” with the training data
  18. 18. BigML, Inc #DutchMLSchool 18 A Simple Hypothesis Space • Suppose we tell our machine to split the data into two parts based on some threshold of some feature • If a data point is on one side of the threshold, we’ll predict the majority class of all the training points on that side • We can measure how many points in the training data would be correctly predicted using this method • This is how good our “fit” is to the training data • The best threshold is the one with the best fit (and we will try them all)
  19. 19. BigML, Inc #DutchMLSchool 19 Back to the Data Minutes Used Last Month’s Bill Calls To Support Website Visits Churn? 104 103,60 0 0 No 124 56,33 1 0 No 56 214,60 2 0 Yes 2410 305,60 0 5 No 536 145,70 0 0 No 234 122,09 0 1 No 201 185,76 1 7 Yes 111 83,60 3 2 No
  20. 20. BigML, Inc #DutchMLSchool 20 Minutes Used > 200 Minutes Used Last Month’s Bill Calls To Support Website Visits Churn? 104 103,60 0 0 No 124 56,33 1 0 No 56 214,60 2 0 Yes 2410 305,60 0 5 No 536 145,70 0 0 No 234 122,09 0 1 No 201 185,76 1 7 Yes 111 83,60 3 2 No
  21. 21. BigML, Inc #DutchMLSchool 21 Website Visits > 0 Minutes Used Last Month’s Bill Calls To Support Website Visits Churn? 104 103,60 0 0 No 124 56,33 1 0 No 56 214,60 2 0 Yes 2410 305,60 0 5 No 536 145,70 0 0 No 234 122,09 0 1 No 201 185,76 1 7 Yes 111 83,60 3 2 No
  22. 22. BigML, Inc #DutchMLSchool 22 Last Bill > $180 Minutes Used Last Month’s Bill Calls To Support Website Visits Churn? 104 103,60 0 0 No 124 56,33 1 0 No 56 214,60 2 0 Yes 2410 305,60 0 5 No 536 145,70 0 0 No 234 122,09 0 1 No 201 185,76 1 7 Yes 111 83,60 3 2 No
  23. 23. BigML, Inc #DutchMLSchool 23 So Far, So Good! • This is basically what machine learning algorithms do • Try a solution and see how well it fits the training data • If “not well”, take some steps to “improve” it • There are many, many different ways of doing it, but this is usually what it boils down to
  24. 24. BigML, Inc #DutchMLSchool 24 Evaluating and Improving
  25. 25. BigML, Inc #DutchMLSchool 25 Now What? • The next thing is to use the training data to test the model • Split the data into training and test sets (machine learning is very good at memorizing the data) • Train a model on the training set • Evaluate it using the test set (or, the “held out data”) • We’ll get to the evaluation tool more fully later on
  26. 26. BigML, Inc #DutchMLSchool 26 And Now? • Is the model good enough? • If not: • Different modeling approaches (model types, parameter tuning) • Better features (more information, transformations of the information you already have) • The more you fiddle with things, the more you contaminate your results (through overfitting) • Thus, if it’s “good enough”, it’s often best to leave it alone
  27. 27. BigML, Inc #DutchMLSchool 27 Explanations
  28. 28. BigML, Inc #DutchMLSchool 28 Field Importance • While our model is good, we don’t really have a good high level overview of why it thinks what it thinks • BigML supervised models provide this in the form of field importance under the model summary report
  29. 29. BigML, Inc #DutchMLSchool 29 Individual Explanations • Individual predictions can be explained as well (as the model’s reasoning for a particular point can be different from the model at large) • Use the magnifying glass in the prediction form
  30. 30. BigML, Inc #DutchMLSchool 30 Two Takeaways • When beginning a machine learning project, the more concrete the goal, the better. Numbers are the lifeblood of analytics so if you can quantify your objective(s), success is unlikely • Machine Learning isn’t the right solution for every problem! Be wary of your algorithm being replaced by a human! • “Before embarking on an ambitious project, try to kill it.” - Edsgar Dijkstra
  31. 31. Co-organized by: Sponsor: Business Partners:

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