Overview of Machine Learning and Feature Engineering
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Machine Learning 101 Tutorial at Strata NYC, Sep 2015 Overview of machine learning models and features. Visualization of feature space and feature engineering methods.
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Overview of Machine Learning and Feature Engineering
- 1. Overview of Machine Learning & Feature Engineering Machine Learning 101 Tutorial Strata + Hadoop World, NYC, Sep 2015 Alice Zheng, Dato 1
- 2. 2 About us Chris DuBois Intro to recommenders Alice Zheng Overview of ML Piotr Teterwak Intro to image search & deep learning Krishna Sridhar Deploying ML as a predictive service Danny Bickson TA Alon Palombo TA
- 3. 3 Why machine learning? Model data. Make predictions. Build intelligent applications.
- 4. Classification Predict amongst a discrete set of classes 4
- 10. Similarity Find things like this 10
- 11. 11 Similar products Product I’m buying Output: other products I might be interested in
- 12. 12 Given image, find similar images http://www.tiltomo.com/
- 13. Recommender systems Learn what I want before I know it 13
- 14. 14
- 15. 15 Playlist recommendations Recommendations form coherent & diverse sequence
- 16. 16 Friend recommendations Users and “items” are of the same type
- 18. 18 Clustering images Goldberger et al. Set of Images
- 19. 19 Clustering web search results
- 20. 20 Machine learning … how? Data Answers I fell in love the instant I laid my eyes on that puppy. His big eyes and playful tail, his soft furry paws, … Many systems Many tools Many teams Lots of methods/jargon
- 21. 21 The machine learning pipeline I fell in love the instant I laid my eyes on that puppy. His big eyes and playful tail, his soft furry paws, … Raw data Features Models Predictions Deploy in production
- 22. 22 Three things to know about ML • Feature = numeric representation of raw data • Model = mathematical “summary” of features • Making something that works = choose the right model and features, given data and task
- 23. Feature = numeric representation of raw data
- 24. 24 Representing natural text It is a puppy and it is extremely cute. What’s important? Phrases? Specific words? Ordering? Subject, object, verb? Classify: puppy or not? Raw Text {“it”:2, “is”:2, “a”:1, “puppy”:1, “and”:1, “extremely”:1, “cute”:1 } Bag of Words
- 25. 25 Representing natural text It is a puppy and it is extremely cute. Classify: puppy or not? Raw Text Bag of Words it 2 they 0 I 1 am 0 how 0 puppy 1 and 1 cat 0 aardvark 0 cute 1 extremely 1 … … Sparse vector representation
- 26. 26 Representing images Image source: “Recognizing and learning object categories,” Li Fei-Fei, Rob Fergus, Anthony Torralba, ICCV 2005—2009. Raw image: millions of RGB triplets, one for each pixel Classify: person or animal? Raw Image Bag of Visual Words
- 27. 27 Representing images Classify: person or animal? Raw Image Deep learning features 3.29 -15 -5.24 48.3 1.36 47.1 - 1.92 36.5 2.83 95.4 -19 -89 5.09 37.8 Dense vector representation
- 28. 28 Feature space in machine learning • Raw data high dimensional vectors • Collection of data points point cloud in feature space • Feature engineering = creating features of the appropriate granularity for the task
- 29. Crudely speaking, mathematicians fall into two categories: the algebraists, who find it easiest to reduce all problems to sets of numbers and variables, and the geometers, who understand the world through shapes. -- Masha Gessen, “Perfect Rigor”
- 30. 30 Algebra vs. Geometry a b c a2 + b2 = c2 Algebra Geometry Pythagorean Theorem (Euclidean space)
- 31. 31 Visualizing a sphere in 2D x2 + y2 = 1 a b c Pythagorean theorem: a2 + b2 = c2 x y 1 1
- 32. 32 Visualizing a sphere in 3D x2 + y2 + z2 = 1 x y z 1 1 1
- 33. 33 Visualizing a sphere in 4D x2 + y2 + z2 + t2 = 1 x y z 1 1 1
- 34. 34 Why are we looking at spheres? = = = = Poincaré Conjecture: All physical objects without holes is “equivalent” to a sphere.
- 35. 35 The power of higher dimensions • A sphere in 4D can model the birth and death process of physical objects • High dimensional features can model many things
- 37. 37 The challenge of high dimension geometry • Feature space can have hundreds to millions of dimensions • In high dimensions, our geometric imagination is limited - Algebra comes to our aid
- 38. 38 Visualizing bag-of-words puppy cute 1 1 I have a puppy and it is extremely cute I have a puppy and it is extremely cute it 1 they 0 I 1 am 0 how 0 puppy 1 and 1 cat 0 aardvark 0 zebra 0 cute 1 extremely 1 … …
- 39. 39 Visualizing bag-of-words puppy cute 1 1 1 extremely I have a puppy and it is extremely cute I have an extremely cute cat I have a cute puppy
- 40. 40 Document point cloud word 1 word 2
- 41. Model = mathematical “summary” of features
- 42. 42 What is a summary? • Data point cloud in feature space • Model = a geometric shape that best “fits” the point cloud
- 43. 43 Clustering model Feature 2 Feature 1 Group data points tightly
- 44. 44 Classification model Feature 2 Feature 1 Decide between two classes
- 45. 45 Regression model Target Feature Fit the target values
- 47. 47 When does bag-of-words fail? puppy cat 2 1 1 have I have a puppy I have a cat I have a kitten Task: find a surface that separates documents about dogs vs. cats Problem: the word “have” adds fluff instead of information I have a dog and I have a pen 1
- 48. 48 Improving on bag-of-words • Idea: “normalize” word counts so that popular words are discounted • Term frequency (tf) = Number of times a terms appears in a document • Inverse document frequency of word (idf) = • N = total number of documents • Tf-idf count = tf x idf
- 49. 49 From BOW to tf-idf puppy cat 2 1 1 have I have a puppy I have a cat I have a kitten idf(puppy) = log 4 idf(cat) = log 4 idf(have) = log 1 = 0 I have a dog and I have a pen 1
- 50. 50 From BOW to tf-idf puppy cat1 have tfidf(puppy) = log 4 tfidf(cat) = log 4 tfidf(have) = 0 I have a dog and I have a pen, I have a kitten 1 log 4 log 4 I have a cat I have a puppy Decision surface Tf-idf flattens uninformative dimensions in the BOW point cloud
- 51. 51 Entry points of feature engineering • Start from data and task - What’s the best text representation for classification? • Start from modeling method - What kind of features does k-means assume? - What does linear regression assume about the data?
- 52. Dato’s Machine Learning Platform
- 53. 53 Dato’s machine learning platform Raw data Features Models Predictions Deploy in production GraphLab Create Dato Distributed Dato Predictive Services
- 54. 54 Data structures for feature engineering Features SFrames User Com. Title Body User Disc. SGraphs
- 55. 55 Machine learning toolkits in GraphLab Create • Classification/regression • Clustering • Recommenders • Deep learning • Similarity search • Data matching • Sentiment analysis • Churn prediction • Frequent pattern mining • And on…
- 56. Demo
- 57. 57 Dimensionality reduction Feature 1 Feature 2 Flatten non-useful features PCA: Find most non-flat linear subspace
- 58. 58 PCA : Principal Component Analysis Center data at origin
- 59. 59 PCA : Principal Component Analysis Find a line, such that the average distance of every data point to the line is minimized. This is the 1st Principal Component
- 60. 60 PCA : Principal Component Analysis Find a 2nd line, - at right angles to the 1st - such that the average distance of every data point to the line is minimized. This is the 2nd Principal Component
- 61. 61 PCA : Principal Component Analysis Find a 3rd line - at right angles to the previous lines - such that the average distance of every data point to the line is minimized. … There can only be as many principle components as the dimensionality of the data.
- 62. Demo
- 63. 63 Coursera Machine Learning Specialization • Learn machine learning in depth • Build and deploy intelligent applications • Year long certification program • Joint project between University of Washington + Dato • Details: https://www.coursera.org/specializations/machine-learning
- 64. 64 Next up today alicez@dato.com @RainyData, #StrataConf 11:30am - Intro to recommenders Chris DuBois 1:30pm - Intro to image search & deep learning Piotr Teterwak 3:30pm - Deploying ML as a predictive service Krishna Sridhar