1. Think Big, Start Smart, Scale Fast
Analytics Communication:
Re-Introducing Complex Models

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• Director of Data Science at Think Big
• I work in the intersection of statistics and technology
• But also business and analytics
• Too often see data scientists limit themselves and their
businesses
Dan Mallinger

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1. Importance of Communication
2. Lost Tools of Analytics Communication
3. Tricks for those in Regulated Environments
4. More Communication
Today

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Not Today

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• Familiar = Clear
• Clear = Explainable
• Explainable = Understood
• Understood = Trustworthy
“Explainable” Model Fallacy

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Better Communication Yields…

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Bad Communication and Black Boxes…

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Why We Should Care:
We Won’t Waste Money
Alas, not even a 250Gb
server was sufficient:
even after waiting
three days, the data
couldn't even be
loaded. […]
Steve said it would be
difficult for managers to
accept a process that
involved sampling.

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hlm.html('Test1', test1_score__eoy~test1_score__boy + ...
is_special_ed * perc_free_lunch ...
other_score * support_rec ...
(is_focal | inst_sid), data=kinder)
Technically this is a regression…
So simple anyone can understand it!
Why We Should Care:
You Can’t Explain Your Models Anyway

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• If your model need to be re-fit every month, it probably has an
eating disorder
• Be a better communicator to yourself
Why We Should Care:
Some of Us Don’t Understand Our Models

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Meet Bob

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• Predicting “Membership” (Not really, this is dummy outcome)
• Pick a “black box” model
• Build understanding
Airline Data

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Danger! Does Your Manager Know What Strata Are?!
Manager Doesn’t Trust Samples?

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• Easy:
sapply(1:5000, function(i) {
rand.rows <- sample.int(nrow(raw),
size=10000)
df <- raw[rand.rows, c(dep.cols, ind.cols)]
m <- nnet(Member~., data=df, size=10)
})
• Easier:
library(bootstrap)
• Bootstrap!
– Simple, but underused
– Resample data, rebuild models
– Parametric and non-parametric
bootstrapping (bias/variance)
Gist of non-parametric: Do it a
bunch of times, treat results as
distribution for CI
Manager Doesn’t Trust Samples?

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Stability of Model

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• Bob has convinced his
manager that his
sampling strategy is
acceptable (Good Job,
Bob!)
• But he hasn’t built trust in
the model
Now What?

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Bob Doesn’t Explain Variables Like This…

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• If X matters, then shuffling it should hurt our model
• Then bootstrap for confidence intervals
• Most R models have a method for this (see caret)
Shining a light into the parameters of our black box
Variable Importance

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Shining a light into the parameters of our black box
Variable Importance: Bob’s Data

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• Similar to variable importance
• How do relationships in our model play out in different settings?
• How much does our model depend on accurate measurement?
Sensitivity and Robustness

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Sensitivity and Robustness Example
My code wasn’t working, so thanks to:
https://beckmw.wordpress.com/2013/10/07/sensitivity-analysis-for-neural-networks/

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More Sensitivity and Robustness
Manual variable permutation in R
library(sensitivity)

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• Bob’s manager has told him that
black box models are not
allowed
• But Bob’s neural net performed
better than anything else. Oh
dear!
Dang!

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• Bob’s work in neural nets can be
leveraged!
• Generically: Prototype selection
• Identify points on the decision
boundary to improve model
• Specifically: Extracting decision
trees from neural nets
Blackbox to Whitebox

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Blackbox to Whitebox: Methodology
“Extracting Decision Trees from Trained Neural Networks” - Krishnan & Bhattacharya
Also: https://github.com/dvro/scikit-protopy

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• Bob has shown how
variables impact his
black box
• He’s shown how they
behave in different
contexts
• He’s show how robust
they are to errors
• But he hasn’t told us why
we should care
Now What?

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Accuracy, False Positive Rates, Confusions matrices are CONSTRUCTS
Metrics and Assessment

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• Enterprises are slow: Predict KPI not KRI
• Give confidence bands, sensitivities, and impact of context changes
• Build a story about the model internals and assumptions; tie to domain
knowledge of audience
• Explainability is up to the modeler, not the model *
• Unless, of course, your regulator says otherwise!
Conclusions

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We’re hiring!
http://thinkbig.teradata.com
Thanks!