Answering the “when” question — On a previous post I made the case that survival analysis is essential for better churn prediction. My main argument was that churn is not a question of “who” but rather of “when”. In the “when” question we ask when will a subscriber churn? Put differently how long does a…

Usually I post about how I instruct machines to translate my ideas into scientific discovery. This time around I’d like to discuss something a bit different: How machines can help turn ideas into art. About a month ago I was given early research access to the DALL-E neural net by…

To churn or not churn — That is not the real question! — One of the main topics I’ve been working on through the years is churn. Churn reduction is a top priority for many companies and correctly identifying it’s root causes can greatly improve their bottom line. Considering how well known and appreciated the churn problem is I’m often perplexed by how…

Don’t believe it? neither did I! — Photo by Ben White on Unsplash So here’s a mind blower: In some cases having more samples can actually reduce model performance. Don’t believe it? Neither did I! Read on to see how I demonstrate that phenomenon using a simulation study. Some context On a recent post on my personal blog I’ve…

R still R still is the tool you want — If you think there’s a typo in the subtitle think JLO instead (: We all know python popularity among DS practitioners has soared over the past few years, signalling both aspiring DS on the one hand and organisations on the other to favour python over R in a snowballing dynamic. …

Why choose between speed and readability when you can have both? — R has many great tools for data wrangling. Two of those are the dplyr and data.table packages. While dplyr has very flexible and intuitive syntax, data.table can be orders of magnitude faster in some scenarios. One of those scenarios is when performing operations over a very large number of groups…

Have you tried using the fread function from data.table package in R? I think multiple benchmarks show it to be way faster than pandas in reading CSV files.

Originally published at https://iyarlin.github.io on April 20, 2020. It is not often that I find myself thinking “man, I wish we had in R that cool python library!” (maybe because I don’t do much NLP/DL). …

Originally published at https://iyarlin.github.io on January 21, 2020. We’ve seen on a previous post that one of the main differences between classic ML and Causal Inference is the additional step of using the correct adjustment set for the predictor features. In order to find the correct adjustment set we need…