![]() ![]() NET Framework 4.7.1 projects all tuple related functionality already included to mscorlib. The first one is System.ValueTuple assembly that was included to my project, but for. NET Framework environment I found couple small issues (not reproduced for. ML.NET doesn’t work on 32-bits (at least, right now), and to avoid any issues you can use Project Properties (Build tab) to choose the platform explicitly: NET Framework project you will need to make sure that your current platform is 64-bits. At the same time, if you compile the latest version of the library you will be able to see that there is an improvement regarding to cross-validation in the pipeline that is implemented already and it should be available in the ( ).Īdding the Microsoft.ML package to. It’s possible to split, but you need to go one level down rather than use high level Pipeline API. For example, the current version (0.1.0) doesn’t allow you to split a dataset for training/testing parts and integrate it to a pipeline. ![]() Looks like that many developers are contributing to the project right now to make a product that is too close to Microsoft internal machine learning library. I would recommend to use this approach if you want to get access to all new features. I am going to use the latest stable release to simplify my post, but you can clone GitHub repository and build all needed assemblies from the source. So, let’s create a basic Console application and use NuGet package manager to add Microsoft.ML package to the project: NET Framework, because I found that package installation is more challenging there compare to. NET Core power, but it should also work on. It means that you can use it on Windows, Mac, Linux platforms utilizing. ML.NET is an open-source and cross-platform framework for. NET developers, I decided to start playing with it using exactly the Titanic datasets. That’s why, when Microsoft announced a new machine learning library for. ![]() The datasets to build the model are still available on Kaggle, and you can download them using the following link: DataSets. The centerpiece of the demo was a model that could help make prediction about your chance to survive on Titanic. Couple years ago, I participated in a series of events for students, where we made some demos about Machine Learning Studio. ![]()
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