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Simplifying assumptions

  1. easy
    1. last time: all code, all math. this time, no code, no math.
    2. simple generalized linear models, not panel data or time series
    3. introductory, practical, short
  2. unstructured == text documents, could concievably take similar approach with audio or visual data if you know equivalent preprocessing dsp or image processing, but I haven't done it

Some questions

  1. motivation?
    1. improve overall predictive power of model
    2. imputation: fill in sparse or missing values in your structured dataset
      1. only have gender or visit reason or other categorical value for a subset
      2. have labeled data for observations outside of your dataset and equivalent corpora
    3. infer relationships between a variable of interest and some latent random variable or variables you believe are represented in some way, obvious or not, in your unstructured data
      1. discussions of interactions with staff
      2. operational incidents
      3. ongoing operational issues
    4. really important to understand whatever your "customer" is going to care most about, of course
  2. what does your structured data look like?
    1. how many observations you got?
    2. sparse, dense?
    3. missing values?
  3. What does your unstructured data look like?
    1. what's the link?
      1. predictions about authors?
      2. about objects of discussion?
      3. 1-to-1 vs 1-to-many (hierarchical)
    2. how much of it you got?
    3. labeled? how?
    4. you have some reason to believe there is valuable information in there somewhere
  4. How important are specific model evaluation techniques?
    1. statistical significance
      1. do you need p-values? do you need all the p-values?
    2. How do you feel about precision and recall? RMSE? RMAE?

preprocessing

  1. bag-of-ngrams
  2. chunking
  3. bag-of-chunks
  4. stopword filtering

techniques

  1. The Kitchen Sink approach
    • just throw it all together and regularize the hell out of it
    • or use some deep learning methods
    • pro: "simple"
    • pro: usually performs reasonably well, often very well
    • con: no simple - or even complicated - answer for "why"
  2. Heuristics
    • pro: simple to explain/interpret
    • con: difficult to capture subtlety/nuance in any real way
  3. Full generative model
    • pro: full probability model
    • pro: you are probably smarter
    • con: difficult to do well
    • con: probably not worth the effort
    • con: time consuming
  4. Meeting halfway...
    1. Sub-classifiers, with predictions used as features in core model
      1. need sub-labels
      2. propagate the uncertainty
    2. unsupervised and semi-supervised "summarization", with results used as features in core model
      1. clustering
      2. abstraction
      3. dimensionality reduction
      4. need lots of data

things to watch out for

  1. econometrics and expectations
  2. interpretation of coefs
  3. significance testing

examples

  1. predict receptiveness of potential targets for a promotional campaign
    • use for rank ordering
    • structured data: demographic info, customer activity
    • unstructured data: social media presence/postings
  2. predicting operational metrics at different locations of a business
    • use for informing business decisions, changes in operations
    • structured data: operational metrics, geographic/region demographics
    • unstructured data: social reviews and survey responses for location
  3. predict merchant upside for participating in a "daily deal" promotion
    • use for rank ordering sales decisions and informing business decisions
    • structured data: operational metrics, geographic/region demographics
    • unstructured data: social reviews and survey responses for location

useful tools

  1. tools matter generally
    1. community, tutorials, etc
    2. SAS user group
    3. can't help you
  2. python: can use first results of first three in statsmodels 0. numpy/scipy
    1. NLTK (comprehensive python NLP package)
    2. scikit-learn (python machine-learning package)
    3. gensim (python topic modeling toolkit)
    4. theano (deep learning)
  3. R
    1. tm (R package)
    2. hadley wickham gives good package
  4. Questions?
    1. everything will be on my personal website: www.obscureanalytics.com
    2. and on github