With the release of v0.7.0, it is time to revisit K-means clustering using F# and Microsoft’s new ML.NET framework. The api has changed enough to warrant a minor rework. This post is a re-examination of a previous post F# and ML.NET Clustering. The use case will be to use examination attributes to classify mammogram results.
Recently I was working on a project using F# and Elasticsearch. I thought it would be fun to post a light introduction. Nicely enough, Elastic provides a .NET client, two actually here. They are a combination of low level and high level interfaces. As is sometimes the case, using F# and C#-style libraries requires some clever navigation of the interface. For this post I will only focus on the high level interface (NEST).
Expanding on my previous post, F# and ML.NET Regression, the current post will take a look at performing classification using Microsoft’s new ML.NET framework. The task at hand will be to use biomechanical attributes to classify patient vertebra conditions into normal (NO), disk hernia (DH), or spondilolysthesis (SL) categories.