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1
The How and Why of Feature Engineering
Alice Zheng, DatoMarch 29, 2016Strata + Hadoop World, San Jose
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My journey so far
Shortage of expertise andgood tools in the market.
Applied machine learning/data science
Build ML tools
Write a book
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Machine learning is great!
Model data.Make predictions.Build intelligent
applications.Play chess and go!
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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
FeaturesModels
Predictions
Deploy inproduction
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If machine learning were hairstyles
Images courtesy of “A visual history of ancient hairdos” and “An animated history of 20th century hairstyles.”
ModelsMagnificent, ornate, high-maintenance
Feature engineeringStreet smart, ad-hoc, hacky
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Making sense of feature engineering• Feature generation• Feature cleaning and transformation• How well do they work?• Why?
Feature GenerationFeature: An individual measurable property of a phenomenon being observed.
⎯ Christopher Bishop, “Pattern Recognition and Machine Learning”
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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
{“it”:2, “is”:2, “a”:1, “puppy”:1, “and”:1, “extremely”:1, “cute”:1 }
Bag of Words
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Representing natural text
It is a puppy and it is extremely cute.
Classify: puppy or not?
Raw Text Bag of Wordsit 2
they 0
I 0
am 0
how 0
puppy 1
and 1
cat 0
aardvark 0
cute 1
extremely 1
… …
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
Classify: person or animal?Raw Image Bag of Visual Words
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Representing imagesClassify: person or animal?Raw Image Deep learning features
3.29-15
-5.2448.31.3647.1
-1.9236.52.8395.4-19-89
5.0937.8
Dense vector representation
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Representing audioRaw Audio Spectrogram
features
Classify: Music or voice?Type of instrument
t=0 t=1 t=2
6.1917 -0.3411 1.2418
0.2205 0.0214 0.4503
1.0423 0.2214 -1.0017
-0.2340 -0.0392 -0.2617
0.2750 0.0226 0.1229
0.0653 0.0428 -0.4721
0.3169 0.0541 -0.1033
-0.2970 -0.0627 0.1960
Time series of dense vectors
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Feature generation for audio, image, text
I fell in love the instant I laid my eyes on that puppy. His big eyes and playful tail, his soft furry paws, …
“Human native”Conceptually abstract
Low Semantic content in dataHigh
Higher Difficulty of feature generationLower
Feature Cleaning and Transformation
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Auto-generated features are noisyRank Word Doc Count Rank Word Doc Count
1 the 1,416,058 11 was 929,703
2 and 1,381,324 12 this 844,824
3 a 1,263,126 13 but 822,313
4 i 1,230,214 14 my 786,595
5 to 1,196,238 15 that 777,045
6 it 1,027,835 16 with 775,044
7 of 1,025,638 17 on 735,419
8 for 993,430 18 they 720,994
9 is 988,547 19 you 701,015
10 in 961,518 20 have 692,749
Most popular words in Yelp reviews dataset (~ 6M reviews).
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Auto-generated features are noisyRank Word Doc Count Rank Word Doc Count
357,480 cmtk8xyqg 1 357,470 attractif 1
357,479 tangified 1 357,469 chappagetti 1
357,478 laaaaaaasts 1 357,468 herdy 1
357,477 bailouts 1 357,467 csmpus 1
357,476 feautred 1 357,466 costoso 1
357,475 résine 1 357,465 freebased 1
357,474 chilyl 1 357,464 tikme 1
357,473 cariottis 1 357,463 traditionresort 1
357,472 enfeebled 1 357,462 jallisco 1
357,471 sparklely 1 357,461 zoawan 1
Least popular words in Yelp reviews dataset (~ 6M reviews).
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Feature cleaning• Popular words and rare words are not helpful• Manually defined blacklist – stopwords
a b c d e f g h iable be came definitely each far get had ieabout became can described edu few gets happens ifabove because cannot despite eg fifth getting hardly ignoredaccording become cant did eight first given has immediatelyaccordingly becomes cause different either five gives have inacross becoming causes do else followed go having inasmuch… … … … … … … … …
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Feature cleaning• Frequency-based pruning
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Stopwords vs. frequency filters
No training required
Stopwords Frequency filters
Can be exhaustive
Inflexible
Adapts to data
Also deals with rare words
Needs tuning, hard to control
Both require manual attention
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Tf-Idf: Automatic “soft” filter• Tf-idf = term frequency x inverse document
frequency• Tf = Number of times a terms appears in a
document• Idf = log(# total docs / # docs containing word w)• Large for uncommon words, small for popular words• Discounts popular words, highlights rare words
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Visualizing bag-of-words
puppy
cat
2
11
have
I have a puppy
I have a catI have a kitten
I have a dogand I have a pen
1
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Visualizing tf-idf
puppy
cat
2
11
have
I have a puppy
I have a catI have a kitten
idf(puppy) = log 4idf(cat) = log 4idf(have) = log 1 = 0
I have a dogand I have a pen
1
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Visualizing tf-idf
puppy
cat1
have
tfidf(puppy) = log 4tfidf(cat) = log 4tfidf(have) = 0
I have a dogand I have a pen,I have a kitten
1
log 4
log 4
I have a cat
I have a puppy
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Algebraically, tf-idf = column scalingw1 w2 w3 w4 … wM
d1
d2
d3
d4
…
dN
idf = log (N/L0 norm of word column)
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w1 w2 w3 w4 … wM
d1
d2
d3
d4
…
dN
Algebraically, tf-idf = column scaling
Multiply word column with scalar (idf of word)
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w1 w2 w3 w4 … wM
d1
d2
d3
d4
…
dN
Algebraically, tf-idf = column scaling
Multiply word column with scalar (idf of word)
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Algebraically, tf-idf = column scalingw1 w2 w3 w4 … wM
d1
d2
d3
d4
…
dN
Multiply word column with scalar (idf of word)
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Other types of column scaling• L2 scaling = divide column by L2 norm
How well do they work?
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Classify reviews using logistic regression• Classify business category of Yelp reviews• Bag-of-words vs. L2 normalization vs. tf-idf• Model: logistic regression
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Observations• l2 regularization made no difference (with proper
tuning)• L2 normalization made no difference on accuracy• Tf-idf did better, but barely• But they are both column scaling methods! Why
the difference?
A Peek Under the Hood
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Linear classification
Feature 2
Feature 1
Find the best line to separate two classes
Algebraically–solve linear systems
Data matrix
Weight vector
Labels
How a matrix works
Any matrixLeft
singular vectors
Singular values Rightsingularvectors
How a matrix works
Any matrix
Project
ScaleProject
How a matrix works
Null space
Singular value = 0
Null space = part of the input space that is squashed by the matrix
Column space
Singular value ≠ 0
Column space = the non-zero part of the output space
Effect of column scaling
Scaled columns
Effect of column scaling
Scaled columns Singular values change(but zeros stay zero)
Singular vectors may also change
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Effect of column scaling• Changes the singular values and vectors, but not
the rank of the null space or column space• … unless the scaling factor is zero
- Could only happen with tf-idf• L2 scaling improves the condition number
(therefore the solver converges faster)
43
Mystery resolved• Tf-idf can emphasize some columns while zeroing
out others—the uninformative features• L2 normalization makes all features equal in “size”
- Improves the condition number of the matrix- Solver converges faster
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Take-away points• Many tricks for feature generation and
transformation• Features interact with models, making their effects
difficult to predict• But so much fun to play with!• New book coming out: Mastering feature
engineering- More tricks, intuition, analysis
@RainyData