Understanding Feature Space in Machine Learning
•Télécharger en tant que PPTX, PDF•
100 j'aime•29,963 vues
An explanation of fundamental concepts of features and models in machine learning, building on our geometric intuition of high dimensional spaces.
Recommandé
Contenu connexe
Tendances
Tendances (20)
En vedette
En vedette (6)
Similaire à Understanding Feature Space in Machine Learning
Similaire à Understanding Feature Space in Machine Learning (20)
Dernier
Dernier (20)
Understanding Feature Space in Machine Learning
- 1. Understanding Feature Space in Machine Learning Alice Zheng, Dato September 9, 2015 1
- 2. 2 My journey so far Applied machine learning (Data science) Build ML tools Shortage of experts and good tools.
- 3. 3 Why machine learning? Model data. Make predictions. Build intelligent applications.
- 4. 4 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
- 5. Feature = numeric representation of raw data
- 6. 6 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
- 7. 7 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
- 8. 8 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
- 9. 9 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
- 10. 10 Feature space in machine learning • Raw data high dimensional vectors • Collection of data points point cloud in feature space • Model = geometric summary of point cloud • Feature engineering = creating features of the appropriate granularity for the task
- 11. 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”
- 12. 12 Algebra vs. Geometry a b c a2 + b2 = c2 Algebra Geometry Pythagorean Theorem (Euclidean space)
- 13. 13 Visualizing a sphere in 2D x2 + y2 = 1 a b c Pythagorean theorem: a2 + b2 = c2 x y 1 1
- 14. 14 Visualizing a sphere in 3D x2 + y2 + z2 = 1 x y z 1 1 1
- 15. 15 Visualizing a sphere in 4D x2 + y2 + z2 + t2 = 1 x y z 1 1 1
- 16. 16 Why are we looking at spheres? = = = = Poincaré Conjecture: All physical objects without holes is “equivalent” to a sphere.
- 17. 17 The power of higher dimensions • A sphere in 4D can model the birth and death process of physical objects • Point clouds = approximate geometric shapes • High dimensional features can model many things
- 19. 19 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
- 20. 20 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 … …
- 21. 21 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
- 22. 22 Document point cloud word 1 word 2
- 23. 23 What is a model? • Model = mathematical “summary” of data • What’s a summary? - A geometric shape
- 24. 24 Classification model Feature 2 Feature 1 Decide between two classes
- 25. 25 Clustering model Feature 2 Feature 1 Group data points tightly
- 26. 26 Regression model Target Feature Fit the target values
- 28. 28 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
- 29. 29 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
- 30. 30 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
- 31. 31 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
- 32. 32 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?
- 33. 33 That’s not all, folks! • There’s a lot more to feature engineering: - Feature normalization - Feature transformations - “Regularizing” models - Learning the right features • Dato is hiring! jobs@dato.com alicez@dato.com @RainyData
Notes de l'éditeur
- Features sit between raw data and model. They can make or break an application.