And the Journey converting “Machine Learning in Action” from Python to F# continues! Rather than following the order of the book, I decided to skip chapters 8 and 9, dedicated to regression methods (regression is something I spent a bit too much time doing in the past to be excited about it just right now), and go straight to Unsupervised Learning, which begins with the K-means clustering algorithm. So what is clustering about? In a nutshell, clustering focuses on the following question: given a set of obs[...]

In spite of being color blind, I am a visual guy – I like to see things. Nothing beats a chart to identify problems in your data. I also spend lots of time manipulating data in FSI, the F# REPL, and while solutions like FSharpChart makes it possible to produce nice graphs fairly easily, I still find it introduces a bit of friction, and wondered how complicated it would be to use Excel as a charting engine. Turns out, it’s not very complicated. The typical use case for generating charts in Excel is to first[...]

Phil Trelford recently released Foq, a small F# mocking library (with a very daring name). If most of your code is in F#, this is probably not a big deal for you, because the technique of mocking isn’t very useful in F# (at least in my experience). On the other hand, if your goal is to unit test some C# code in F#, then Foq comes in very handy. So why would you want to write your unit tests in F# in the first place? Let’s start with some plain old C# code, like this:namespace CodeBase { using System; [...]

I am still toying with the idea of using FSI from within Excel – wouldn’t it be nice if, instead of having to resort to VBA or C# via VSTO, I could leverage F#, with unfettered access to .NET and a nice scripting language, while having at my disposal things like the charting abilities of Excel? Judging from the discussion on Twitter this morning, it seems I am not the only one to like the idea of F# in Excel: @jonharrop @dnesteruk @7sharp9 @dsyme the F# plugin for MonoDevelop & tryfsharp.org can provide a[...]

This post continues my journey converting the Python samples from Machine Learning in Action into F#. On the program today: chapter 7, dedicated to AdaBoost. This is also the last chapter revolving around classification. After almost 6 months spending my week-ends on classifiers, I am rather glad to change gears a bit! The idea behind the algorithm Algorithm outline AdaBoost is short for “Adaptative Boosting”. Boosting is based on a very common-sense idea: instead of trying to find one perfect classifier t[...]