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!