The Microsoft ML.NET library is a C# language code library for machine learning. ML.NET has been under development and was available in pre-release form for quite some time, but was finally released for public use as version 1.2 a few weeks ago. The motivation for ML.NET is that Python is the fact the default language for machine learning but C# is the default language for applications on a Windows platform and this makes it very difficult to integrate a trained Python model into a C# application. With the ML.NET library, C# developers can create a model in C# and easily drop it into a C# application.

A few weeks ago I used the AutoML tool to automatically generate a ML.NET model. It worked very nicely, but the auto-generated code wasn’t totally clean and efficient. So I set out to refactor the demo I created using AutoML, by coding an ML.NET program from scratch.

My demo problem was to predict employee job Satisfaction (low, medium, high) from Age, Hourly/Salaried status, job Role (tech, management, sales), and annual Income.

The demo code took me about 4 hours to write, which isn’t too bad. But I had a head start because I’ve been looking at ML.NET for many months. Additionally, ML.NET is derived from an internal Microsoft library named TLC, which in turn was derived from an internal library named TMSN, and I’d used TMSN and TLC many times.

Writing ML.NET code from scratch wasn’t trivial but machine learning problems aren’t trivial. It will be interesting to see if ML.NET catches on in the developer community, or if ML.NET falls by the wayside like so many other code libraries and framewoks. I like ML.NET quite a bit but I’m not representative of the target audience.

My example prediction model uses “Fast Trees” — an algorithm that’s not easy to do without a code library.

It’s not easy to make trees look interesting. I think these four examples by four different artists succeeded in making nice, interesting images of trees.