Get Out of My Dreams...
...and Into My Model.
Incorporating Unstructured Data Into Your Feature Set
Rob Mealey
Machine Learning @ NewBrand
Nov 20, 2014
Making it easy for myself...
meaning at minimum I will definitely annoy the PhD's in the room...
just a smidge of false advertising...
First, a Few Questions...
What's your goal here?
| "MALE" | 22 |
| "FEMALE" | 35 |
| "Female" | 40 |
| "male" | 212 |
| "malEe" | 10 |
| "Emale" | 191 |
| "f" | 142 |
| "m" | 220 |
| "Unknown" | 10040 |
| "XXXX" | 8020 |
| "Michigan" | 20 |
What does your boss care about?
What do your structured data look like?
What does your structured data look like?
How much of it you got?
Sparse? Dense?
What does your corpus look like?
What's the link?
big data? data stream? data lake?
Got labels?
how'd you get em?
There is some there there, right?
Last question: Model evaluation?
Preprocessing
bag-of-ngrams
chunking
stopword filtering
What do you mean, specification?
Heuristics
"dirty" vs "clean"
"rust"
"rude"
| DocId | DIRTY | RUDE | CLEAN | ... |
|---|---|---|---|---|
| DOC1 | 0 | 1 | 0 | ... |
| DOC2 | 0 | 1 | 0 | ... |
| DOC2 | 1 | 0 | 1 | ... |
"OMFG, is that a quadruple negative?"
Total. Probability.
You should check if you've been fired.
Also, it is 2037 now.
PyMC, BUGS/JAGS, STAN
the middle roads...
sub-classifiers
need good labels...
inter-annotator agreement?
use the uncertainty...
GIGO!
Also YOLO.
semi- or unsupervised methods
dimensionality reduction
clustering
topic modeling
probabilistic or otherwise
need lots of the data...
Gotchas
"endogeneity"
If you are an econometrician,
read this paper:
Regression and Causation:
A Critical Examination of Six Econometrics Textbooks by Bryant Chen and Judea Pearl
mind your betas...
you thought they were hard to interpret before?
Task:
predict receptiveness of potential targets for a promotional campaign.
Structured Data:
- demographics
- purchase history
- ?
Unstructured Data:
Streams of "Chirps"
Links: one "row" of structured data to many "documents"
Links: classifying document/content authors
Kitchen sink approach...
also can fill in demographic holes, as mentioned before
Task:
predict operational metrics at different locations of a service business
Task:
predict merchant upside for participating in a "daily deal" promotion
What AND why.
tools matter...
because you're probably going to need some help...
How many SASonistas we got in the house?
What about Stataniacs?
numpy/scipy
CountVectorizer
HashingVectorizer
"tm" package
CRAN Task View: Natural Language Processing