# Understanding Feature Space in Machine Learning

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Understanding Feature Space in Machine Learning Alice Zheng, Dato September 9, 2015 1

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My journey so far Applied machine learning (Data science) Build ML tools

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Why machine learning? Model data. Make predictions. Build intelligent applications.

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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

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Feature = numeric representation of raw data

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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

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Representing natural text It is a puppy and it is extremely cute. Classify: puppy or not? Raw Text Sparse vector representation

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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 Raw Image

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Representing images Raw Image Deep learning features 3.29 -15 -5.24 48.3 1.36 47.1 -1.9236.5 2.83 95.4 -19 -89 5.09 37.8 Dense vector representation

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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

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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”

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Algebra vs. Geometry a b c a2 + b2 = c2 Algebra Geometry (Euclidean space)

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Visualizing a sphere in 2D x2 + y2 = 1

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Visualizing a sphere in 3D x2 + y2 + z2 = 1 x y z 1 1 1

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Visualizing a sphere in 4D x2 + y2 + z2 + t2 = 1 x y z 1 1 1

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Why are we looking at spheres? = = = = Poincare Conjecture: All physical objects without holes is “equivalent” to a sphere.

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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

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Visualizing Feature Space

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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

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Visualizing bag-of-words I have a puppy and it is extremely cute

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Visualizing bag-of-words puppy cute 1 1 1 extremely

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Document point cloud word 1 word 2

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What is a model? Model = mathematical “summary” of data What’s a summary? A geometric shape

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Classification model Feature 2 Feature 1 Decide between two classes

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Clustering model Feature 2 Feature 1 Group data points tightly

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Regression model Target Feature Fit the target values

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Visualizing Feature Engineering

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When does bag-of-words fail? puppy cat 2 1 1 have Task: find a surface that separates documents about dogs vs. cats Problem: the word “have” adds fluff instead of information 1

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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

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From BOW to tf-idf puppy cat 2 1 1 have idf(puppy) = log 4 idf(cat) = log 4 idf(have) = log 1 = 0 1

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From BOW to tf-idf puppy cat 1 have tfidf(puppy) = log 4 tfidf(cat) = log 4 tfidf(have) = 0 1 log 4 log 4 Tf-idf flattens uninformative dimensions in the BOW point cloud

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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?

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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! [email protected] [email protected] @RainyData

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