Feature Engineering for Machine Learning at QConSP
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The document discusses feature engineering for machine learning, emphasizing its importance in transforming raw data into suitable formats for models. It outlines the process of data science, addresses ethics in data usage, and provides guidelines for effective feature selection and transformation. The author also touches on techniques such as normalization and natural language processing (NLP) for enhancing data representation.
![when we say…
DATA SCIENCE
• …. the interdisciplinary intersection of methods, processes,
algorithms and problem solving techniques to extract
knowledge from data1
MACHINE LEARNING [ML]
§ …. fitting mathematical models to data in order to
derive insights or make predictions.2
FEATURE
§ …. a numeric representation of an aspect of raw data2
FEATURE ENGINEERING
§ …. the act of extracting features from raw data and
transforming them into formats that are suitable for the
machine learning model2
hint: our community is well represented in Wikipedia @amcasari](https://image.slidesharecdn.com/featureengineeringformachinelearning-qconsp-amcasari-180514202251/85/Feature-Engineering-for-Machine-Learning-at-QConSP-8-320.jpg)
![[n.b. ethics]
DATA
SOCIAL CONSTRUCT
§ …. “jointly constructed understandings of the world that
form the basis for shared assumptions about reality”1
BIAS
§ … results from unfair sampling of a population, or from an
estimation process that does not give accurate results on
average2
ACCOUNTABILITY
§ … you are answerable for your decisions and obligated
to be able to explain the resulting consequences3
hint: much more about this w/ @kjam at 14:30
§ …. is an abstract representation of reality, not reality itself.
Data is a part of the system of record, but not the actual
system itself.
@amcasari](https://image.slidesharecdn.com/featureengineeringformachinelearning-qconsp-amcasari-180514202251/85/Feature-Engineering-for-Machine-Learning-at-QConSP-9-320.jpg)
![vector space
scalar: single numeric
feature
vector: ordered list of
scalars
Example:
1/ two-dimensional
vector, v = [1, -1]
@amcasari](https://image.slidesharecdn.com/featureengineeringformachinelearning-qconsp-amcasari-180514202251/85/Feature-Engineering-for-Machine-Learning-at-QConSP-11-320.jpg)
Feature Engineering for Machine Learning at QConSP
- 1. Feature Engineering for Machine Learning Amanda Casari Principal Product Manager + Data Scientist Concur Labs @ SAP Concur @amcasari
- 2. here to there via random walk product + data @ SAP Concur control systems engineering + robotics + legos officer in US Navy operations research analyst wandering dirtbag + conservation volunteer EE + applied math + complex systems underwater robotics consultant extraordinaire stay at home mom co-author NASA Datanaut @amcasarihere to there via random walk
- 3. data science is not magic… @amcasari
- 4. …but it is a process (sometimes painful) @amcasari@MROGATI
- 5. it is easy to get turned around…. @amcasari idea research exploration hypotheses model outcomes feedback
- 6. …and it is easy to get mixed up xkcd #1838 @amcasari
- 7. …so let’s focus on getting from data to models feature engineering goes here! @amcasari
- 8. when we say… DATA SCIENCE • …. the interdisciplinary intersection of methods, processes, algorithms and problem solving techniques to extract knowledge from data1 MACHINE LEARNING [ML] § …. fitting mathematical models to data in order to derive insights or make predictions.2 FEATURE § …. a numeric representation of an aspect of raw data2 FEATURE ENGINEERING § …. the act of extracting features from raw data and transforming them into formats that are suitable for the machine learning model2 hint: our community is well represented in Wikipedia @amcasari
- 9. [n.b. ethics] DATA SOCIAL CONSTRUCT § …. “jointly constructed understandings of the world that form the basis for shared assumptions about reality”1 BIAS § … results from unfair sampling of a population, or from an estimation process that does not give accurate results on average2 ACCOUNTABILITY § … you are answerable for your decisions and obligated to be able to explain the resulting consequences3 hint: much more about this w/ @kjam at 14:30 § …. is an abstract representation of reality, not reality itself. Data is a part of the system of record, but not the actual system itself. @amcasari
- 10. how to choose? 1/ FRAME YOUR PROBLEM 2/ UNDERSTAND YOUR DATA § What data will be most helpful to understand and generate a better understanding of this problem? 3/ FRAME YOUR FEATURE GOALS § What are you optimizing for? § Iteration speed § Model performance 4 / TEST, ITERATE, TEST AGAIN § Check your choices for robustness § Validate but realize this will still change § Can you frame your problem in a way that machine learning could be useful? e.g. prediction @amcasari
- 11. vector space scalar: single numeric feature vector: ordered list of scalars Example: 1/ two-dimensional vector, v = [1, -1] @amcasari
- 12. feature space In data, abstract vectors take on actual meaning Examples: • 1/ a vector can represent a person’s preference for songs • Song = feature • +1: Thumbs-up • -1: Thumbs-down • 2/ song represents ind. preferences in a group @amcasari
- 13. Counts: Fancy Tricks with Simple Numbers
- 16. counts: fixed width binning @amcasari
- 18. @amcasari loga(ax) = x, where a is a positive constant and x can be any positive number a0=1, loga(1)=0 tl;dr the log function compresses the range of large numbers and expands the range of small numbers counts: log transform binning
- 19. @amcasari What does scaling do for features?
- 23. @amcasari proper scaling preserves underlying shape
- 24. Text: Flatten, Filter, Chunk
- 27. filter: frequency based filtering (stopwords) @amcasari These NLP libraries have both English + Portuguese corpora, models, etc 1/ spacy 2/ NLTK 3/ OpenNLP
- 28. chunk: parts of speech matter @amcasari Pop Chart Lab, npr.org
- 29. @amcasari thank you @RainyData code repobuy the book here!