From the version 0.8 it's possible with ML.NET to evaluate features importance and so understand what are the columns that are more important to predict the final value. Permutation Feature Importance has this phurpose, highlight the most important features in order to understand what features has to be included or not; excluding some features from... Continue Reading →

As we have seen in the previous post, in order to use ML.NET we have to configure the pipeline with all the operations that we need to do to transform our dataset and the algorithm that we want to use. In my opinion this syntax is rather verbose and of course has to be repeated... Continue Reading →

Train a ML.NET algorithm is the activity that we need to do when we want to prepare and algorithm to predict some values. As we'll see we have to prepare the pipeline with some operations that will be propedeutic to the training, like load the dataset, convert alphanumeric colums and so on. Now that we... Continue Reading →

A tipical question when we approach a machine learning problem is: what is the algorithm that fits better in my dataset? In the previous post we have seen the possible classifications for the ML.NET algorithms and this is the first step to restrict the possible choices. Now we can make considerations about what are our... Continue Reading →

ML.NET is a new open source project released by Microsoft few months ago implemented for .NET applications and available as a NuGet package. Now in version 0.4 is growing rapidly but it already has a significative number of algorithms that show the power of the framework. It's a group of libraries used internally by microsoft... Continue Reading →