Transmogrification: The Magic of Feature Engineering Leah McGuire and Mayukh Bhaowal
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1 Transmogrification: The Magic of Feature Engineering Leah McGuire and Mayukh Bhaowal
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6 ML algorithms take center stage in AI Modeling Raw Data Feature Engineering Bottleneck
7 Mythical Numeric Matrix X X2 X3 X4 X5 Y A B B A A
8 Use the data types
9 Automatic Feature Engineering Numeric Categorical Text Temporal Spatial Time difference Augment with external data e.g avg income Imputation Imputation Track null value Log transformation for large range Scaling - znormalize Smart Binning Track null value One Hot Encoding Dynamic Top K pivot Smart Binning LabelCount Encoding Category Embedding Tokenization Hash Encoding Circular Statistics Tf-Idf Word2Vec Sentiment Analysis Language Detection Time extraction (day, week, month, year) Closeness to major events Spatial fraudulent behavior e.g: impossible travel speed Geo-encoding
10 Transmogrification val featurevector = Seq(age, phone, , subject, zipcode).transmogrify()
11 Impact on Feature Engineering Is Spammy Top Domain Phone Country Code Age Phone Is Valid Age [-5] Vector Subject Age [5-35] Age [>35] Zipcode Top TF-IDF Terms Average Income
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14 The Black Swan of Perfectly Interpretable Models Leah McGuire, Mayukh Bhaowal
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16 Roadmap for this talk What does it mean to explain your model? Why explain your model? How to explain your model? Interpretability vs accuracy tradeoff Complications of feature engineering Global (full model) solutions Local (record level) solutions
17 Roadmap for this talk What does it mean to explain your model? Why explain your model? How to explain your model? Interpretability vs accuracy tradeoff Complications of feature engineering Global (full model) solutions Local (record level) solutions
18 The Question Why did the machine learning model make the decision that it did?
19 Translation # How do I fix this model? Data Scientist
20 Translation #2 Do we have our bases covered, in case of a regulatory audit? Legal Counsel
21 Translation #3 Does Einstein know what I know? How do I use this prediction? Non Technical End User
22 P(c f) Input Pk(c f) Pn(c f) Output Σ
23 Model Insights Report
24 Roadmap for this talk What does it mean to explain your model? Why explain your model? How to explain your model? Interpretability vs accuracy tradeoff Complications of feature engineering Global (full model) solutions Local (record level) solutions
25 Debuggability Top contributing features for surviving the Titanic: F. Gender 2. pclass 3. Body
26 Trust How can you trust a man that wears both a belt and suspenders? Man can't even trust his own pants.
27 Right Human Machine Wrong
28 Bias
29 Legal
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31 Black defendant has higher risk scores
32 Actionable
33 Roadmap for this talk What does it mean to explain your model? Why explain your model? How to explain your model? Interpretability vs accuracy tradeoff Complications of feature engineering Global (full model) solutions Local (record level) solutions
34 It s complicated
35 Can you use a simple model? Feature Weights/ Importance Global Feature Weights/ Importance Local Are the raw features fed into the model interpretable? Does the consumer care about how features affect the model or just feature insights? Does the consumer care about individual predictions? Feature Impact Model Agnostic Global Secondary Model Global Feature Impact Model Agnostic Local Secondary Model Local
36 Roadmap for this talk What does it mean to explain your model? Why explain your model? How to explain your model? Interpretability vs accuracy tradeoff Complications of feature engineering Global (full model) solutions Local (record level) solutions
37 The best model or the model you can explain?
38 Roadmap for this talk What does it mean to explain your model? Why explain your model? How to explain your model? Interpretability vs accuracy tradeoff Complications of feature engineering Global (full model) solutions Local (record level) solutions
39 Where did you get the feature matrix? X X2 X3 X4 X5 Y A B B A A
40 Feature Engineering Is Spammy Top Domain Phone Country Code Age Phone Is Valid Age [-5] Vector Subject Age [5-35] Age [>35] Zipcode Top TF-IDF Terms Average Income
41 Metadata!!! The name of the feature the column was made from The name of the RAW feature(s) the column was made from Everything you did to get the column Any grouping information across columns Description of the value in the column
42 Roadmap for this talk What does it mean to explain your model? Why explain your model? How to explain your model? Interpretability vs accuracy tradeoff Complications of feature engineering Global (full model) solutions Local (record level) solutions
43 Interpretability: Global vs Local
44 Can you use a simple model? Feature Weights/ Importance Global Are the raw features fed into the model interpretable? Does the consumer care about how features affect the model or just feature insights? Feature Impact Model Agnostic Global Does the consumer care about individual predictions? Secondary Model Global
45 Feature Weight / Importance (Global)
46 Predict House Price
47 Predict Titanic Passenger Survival
48 P(c f) Input Pk(c f) Pn(c f) Output Σ
49 Feature Impact (Global - the hard way) X X2 X3 X4 X5 Y A B B A A
50 Feature Impact (Global - the hard way)
51 Issues with Feature Importance / Weight / Impact (Global)
52 Secondary Model Prediction Input Explanation
53 Secondary Model (Global)
54 Secondary Model (Global)
55 What we do: All the metadata about how you got the feature Correlation Mutual information Feature weight / importance Feature distribution
56 { "featurename" : "sex", What "derivedfeatures" : [ { "stagesapplied" : [ "pivottext_opsetvectorizer" ], "derivedfeaturevalue" : "Male", "corr" : , "mutualinformation" : , "contribution" : ,. }, { "stagesapplied" : [ "pivottext_opsetvectorizer" ], "derivedfeaturevalue" : "Female", "corr" : , "mutualinformation" : , "contribution" : ,. } } we do:
57 Roadmap for this talk What does it mean to explain your model? Why explain your model? How to explain your model? Interpretability vs accuracy tradeoff Complications of feature engineering Global (full model) solutions Local (record level) solutions
58 Can you use a simple model? Feature Weights/ Importance Local Are the raw features fed into the model interpretable? Does the consumer care about how features affect the model or just feature insights? Feature Impact Model Agnostic Local Does the consumer care about individual predictions? Secondary Model Local
59 Feature Weight (Local)
60 Predict House Price
61 Feature Weight (Local)
62 Feature Impact (LOCO) {"age":7., "embarked":"c", "name":"attalah, Miss. Malake", "pclass":"3", "parch":"", "sex":"female", "sibsp":"", "survived":., "ticket":"2627"} Score =.62 Why? sex = "female" (+.3), pclass = 3 (-.5),...
63 Secondary Model (LIME)
64 Secondary Model (Correlation) Norm (feature) * Corr
65 What we do: Use case determines LOCO or correlation Use case determines what level of features we show
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