The Living Thing / Notebooks : Multiple testing

How to go data mining for models without “dredging” for models. (accidentally or otherwise) If you keep on testing models until you find some that fit (which you usually will) how do you know that the fit is in some sense interesting? How sharp will your conclusions be? How does it work when you are testing against a possibly uncountable continuum of hypotheses? (One perspective on sparsity penalties is precisely this, I am told.)

Model selection is this writ small - when you are testing how many variables to include in your model.

In modern high-dimensional models, where you have potentially many explanatory variables, the question of how to handle the combinatorial explosion of possible variables to include, this can also be considered a multiple testing problem. We tend to regard this as a smoothing and model selection problem though.

This all gets more complicated when you think about many people testing many hypothesese in many different experiments then you are going to run into many more issues than just these - also publication bias and suchlike.

Nice hack:

The reusable holdout: Preserving validity in adaptive data analysis which, like everything these days, uses differential privacy methods. Soon I will have my smoothies made by ensuring differential privacy for my bananas’ identities.

Suggestive connection:

Moritz Hardt, The machine learning leaderboard problem:

In this post, I will describe a method to climb the public leaderboard without even looking at the data. The algorithm is so simple and natural that an unwitting analyst might just run it. We will see that in Kaggle’s famous Heritage Health Prize competition this might have propelled a participant from rank around 150 into the top 10 on the public leaderboard without making progress on the actual problem.[…]

I get super excited. I keep climbing the leaderboard! Who would’ve thought that this machine learning thing was so easy? So, I go write a blog post on Medium about Big Data and score a job at, the latest data science startup in the city. Life is pretty sweet. I pick up indoor rock climbing, sign up for wood working classes; I read Proust and books about espresso. Two months later the competition closes and Kaggle releases the final score. What an embarrassment! Wacky boosting did nothing whatsoever on the final test set. I get fired from days before the buyout. My spouse dumps me. The lease expires. I get evicted from my apartment in the Mission. Inevitably, I hike the Pacific Crest Trail and write a novel about it.

See BlHa15 and DFHP15 for more of that.

P-value hacking

Sub topic.

To read

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