Category: Data Science

Nikhil Babar has an introduction to the Levenshtein distance algorithm:

The Levenshtein distance is a string metric for measuring the difference between two sequences. Informally, the Levenshtein distance between two words is the minimum number of single-character edits (i.e. insertions, deletions, or substitutions) required to change one word into the other. It is named after Vladimir Levenshtein, who discovered this equation in 1965.

Levenshtein distance may also be referred to as edit distance, although it may also denote a larger family of distance metrics. It is closely related to pairwise string alignments.

Read on for an explanation and example.  Levenshtein is a great way of calculating string similarities, possibly helping you with tasks like data cleansing by finding typos or alternate spellings, or matching down parts of street addresses.

Giovanni Lanzani has a post with the controversial title above:

Some data is gathered, given to data scientists, and — after two weeks — the first demo takes place. The results are promising, but they need a bit more time.

Fine. After all, the data was messy: they had to clean it up and go back to the source a couple of times.

Two weeks pass and the new results are even nicer. With 70% accuracy, they can predict if a patient will go home after their visit to the emergency room.

This is much better than random (50%)! A full-fledged pilot starts.

They are faced with a couple of challenges to go from model to data product:

• How to send the source data to the model is unclear;

• Where the model should run;

• The hospital operations need to change, as the intake happens with pen and paper;

• They realize that without knowing to which department the patient will go, they won’t add any value;

• To predict the department, the model need the diagnosis. But once the diagnosis gets typed in the computer, the patient has reached their destination: the model is useless!

I think it’s a fair point:  it’s easy from the standpoint of internal researchers to look for things which they can do, but which don’t have much business value.  The risk on the other side is that you’ll start diving into a high-potential-value problem and then realize that the data isn’t there to draw conclusions or that the relationships you expected simply aren’t there.

Vincent Granville discusses the problem of p-hacking:

I read an article this morning, about a top Cornell food researcher having 13 studies retracted, see here. It prompted me to write this blog. It is about data science charlatans and unethical researchers in the Academia, destroying the value of p-values again, using a well known trick called p-hacking, to get published in top journals and get grant money or tenure. The issue is widespread, not just in academic circles, and make people question the validity of scientific methods. It fuels the fake “theories” of those who have lost faith in science.

The trick consists of repeating an experiment sufficiently many times, until the conclusions fit with your agenda. Or by being cherry-picking about the data you use, or even discarding observations deemed to have a negative impact on conclusions. Sometimes, causation and correlations are mixed up on purpose, or misleading charts are displayed. Sometimes, the author lacks statistical acumen.

Usually, these experiments are not reproducible. Even top journals sometimes accept these articles, due to

• Poor peer-review process

• Incentives to publish sensational material

Wansink is a charlatan.  But beyond p-hacking is Andrew Gelman and Eric Loken’s Garden of Forking Paths.  Gelman’s blog, incidentally (example), is where I originally learned about Wansink’s shady behaviors.  Gelman also warns us not to focus on the procedural, but instead on a deeper problem.