Browsed by
Tag: machine learning

Logo UEFA Euro 2020
Using R to build predictions for UEFA Euro 2020

Using R to build predictions for UEFA Euro 2020

Last friday, Euro 2020, one of the biggest events in International soccer, was kicked off by the inaugural match between Italy and Turkey (Italy won it 3-0). Euros (short for European Championships) are usually held every 4 years, but because of he-who-must-not-be-named, last year’s edition was postponed to this summer, while keeping the name “Euro 2020” (much like the Tokyo Olympics). 4 5 years ago, for Euro 2016, I basically wanted to try some cool methods based on splines on…

Read More Read More

Have you checked your features distributions lately?

Have you checked your features distributions lately?

tl;dr Trying to debug a poorly performing machine learning model, I discovered that the distribution of one of the features varied from one date to another. I used a simple and neat affine rescaling. This simple quality improvement brought down the model’s prediction error by a factor 8 Data quality trumps any algorithm I was recently working on a cool dataset that looked unusually friendly. It was tidy, neat, interesting… the kind of things that you rarely encounter in the wild!…

Read More Read More

Bad recommendations, good algorithm

Bad recommendations, good algorithm

If you’ve ever shopped online (*cough* Amazon *cough*), you’ve probably experienced the “vacuum cleaner effect”. You carefully buy one expensive item (e.g. a vacuum cleaner) and then you receive dozens of recommendations for other vacuum cleaners to buy: by email, everywhere on the retailer’s website, or sometimes in the ads you see on other websites. In other terms, Amazon is a 1 trillion dollar company that employs hundreds of data scientists and is incapable of understanding that if you bought…

Read More Read More

Weighting tricks for machine learning with Icarus – Part 1

Weighting tricks for machine learning with Icarus – Part 1

Calibration in survey sampling is a wonderful tool, and today I want to show you how we can use it in some Machine Learning applications, using the R package Icarus. And because ’tis the season, what better than a soccer dataset to illustrate this? The data and code are located on this gitlab repo: https://gitlab.com/haroine/weighting-ml First, let’s start by installing and loading icarus and nnet, the two packages needed in this tutorial, from CRAN (if necessary): install.packages(c(“icarus”,”nnet”)) library(icarus) library(nnet) Then…

Read More Read More