Improving ML.NET model accurancy

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 →

Choosing an ML.NET algorithm

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 →

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