Mistakes I've MadeMistakes I've Made

PyData Seattle 2015Cam Davidson-Pilon

Who am I?Who am I?

Cam Davidson-Pilon

- Lead on the Data Team at Shopify

- Open source contributer

- Author of Bayesian Methods for Hackers(in print soon!)

Ottawa

Ottawa

Ottawa?

Case Study 1Case Study 1

We needed to predict mail returnrates based on census data.

Sample Data (simplified):

Well I'm predicting the rate, so Ibuild that:

Don't need margin of errors...

...then do "data science"

Outcome: failure

What went wrong? At the time, ¯\_(ツ)_/¯

(highly, highly recommended!)

σ =X̄ √nσ

σ =X̄ √nσ

"The std. deviation of the sample mean isequal to the std. deviation of thepopulation over square-root n"

What I learned1. Sample sizes are so important when dealing with

aggregate level data.2. It was only an issue because the sample sizes were

different, too.3. Use the Margin of Error, don't ignore it - it's there for a

reason.4. I got burned so bad here, I became a Bayesian soon after.

Case Study 2Case Study 2

A intra-day time series of S&P, Dow,Nasdaq and FTSE (UK index)

Suppose you areinterested in doing someday trading. Your target:

UK stocks.

Futures on the FTSE inparticular.

Post Backtesting Results

Push to Production -investing really money

What happened?

Data Leakage happened

What I learned1. Your backtesting / cross validation will always be equal or

overly optimistic - plan for that.2. Understand where your data comes from, from start to

finish.

Case Study 3Case Study 3

What I learned1. When developing statistical software that already exists in

the wild, write tests against the output of that software. 2. Be responsible for your software:

Case Study 4Case Study 4

It was my first A/B test atShopify...

Control group: 4%Experiment group: 5%

Bayesian A/B testing told me there was asignificant statistical difference between

the groups...

Upper management wantedto know the relative increase...

(5% - 4%) / 4% = 25%

No.We forgot sample size

again.

What I learned1. Don't naively compute stats on top of stats - this only

compounds the uncertainty. 2. Better to underestimate than overestimate3. Visualizing uncertainty is a the role of a statistician.

Machine LearningMachine Learningcounter examplescounter examples

Sparse-ing thesolution naively

Coefficients after linear regression*:

*Assume data has been normalized too,i.e. mean 0 and standard deviation 1

Decide to drop a variable:

Suppose this is the true model...

Okay, out regression got the coefficientsright, but...

So actually, together, these variables havevery little contribution to Y!

Solution:

Any form of regularization will solve this.For example, using ridge regression withwith even the slightest penalizer gives:

PCA beforeRegression

PCA is great at many things, but it canactually significantly hurt regression if

used as a preprocessing step. How?

Suppose we wish to regress Y onto X and W.The true model of Y is Y = X - W. We don't know this

yet.

Suppose further there is a positivecorrelation between X and W, say 0.5.

Apply PCA to [X W], we get a new matrix:

[ X + W , X − W ]√21

√21

√21

√21

[ X + W , X − W ]√21

√21

√21

√21

Textbook analysis tells you to drop thesecond dimension from this new PCA.

[ X + W ]√21

√21

So now we are regressing Y onto:

i.e., find values to fit the model:

Y = α + β(X + W )

But there are no good values for theseunknowns!

QuickIPythonDemo

Solution:

Don't use naive PCA before regression, youare losing information - try something like

supervised PCA, or just don't do it.

Thanks for listening :)

@cmrn_dp