There are plenty of methods to choose from for classification problems, all with their own strengths and weaknesses. This post will try to compare three of the more basic ones: linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), and logistic regression.
This is the first of probably two posts detailing the construction of an RShiny app. The app in question is meant to take data from two Spotify playlists, make recommendations for tracks from one – which I’ll call the “target” playlist – based on the contents of another – the “reference” playlist. I don’t expect this to be comparable in ability to Spotify’s own system (or anything else, really), but it seems like it should be interesting.
My code is here.
I’ve seen a number of examples of MCMC algorithms, and while they’re all solid, a lot of them tend to be a bit too neat - they have a fairly simple model, a single predictor (maybe two), and not much else. This one is a good example, as it covers the theory in detail, but it’s using an obviously toy data set. So I decided to throw together a slightly more intricate example, highlighting a couple of issues and tricks worth noting for a handwritten implementation.
Note that this post is written under the assumption that the reader already has some knowledge about what MCMC is generally for and broadly how it works. This post is all R code (see here), with no JAGS or BUGS or such. The tidyverse, patchwork, and ISLR libraries are required – the former two for the plots, the latter for the data set used.
I had occasion a while back to try to do a random forest prediction in C. This is a highly situational need – I only did it because I needed to get a random forest that could work with other stuff written in C, no Python allowed – but it was interesting to try to pull apart scikit-learn’s RandomForestRegressor and restructure it in another way.