Using multiple algorithms and tuning the algorithms to find the optimum value for each parameter also improves the accuracy of the model. However, it is not necessary that higher accuracy models always give the accurate results, as sometimes, the improvement in model’s accuracy can be due to over-fitting too.
Where would you find all three (AI, ML and DS) at work? The most common place today is in autonomous driving vehicles. All three disciplines work together to help train an algorithm to recognize obstacles (MS), then to provide real-time actions (AI) to the vehicle, all based on large amounts of information that data science (DS) can analyze.
ML.NET enables developers to implement machine learning tasks like classification, regression, clustering, and recommendation ... etc. In this post, I will show you how to get started with ML.NET implementing permutation feature importance for employee attrition.
I find that machine learning experiment’s results are always interesting and somewhat unexpected in certain cases. On this comparison, the feature ranking results of PFI are often different from the feature selection statistics that are utilized before a model is created. This is useful in many cases, especially when training “black-box” models where it is difficult to explain how the model characterizes the relationship between the features and the target variable.