Machine Learning – Lessons from our POC

Premier Developer April 10, 2020 Apr 10, 2020 04/10/20

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.

Exploring Feature Weights using R and Azure Machine Learning Studio

Premier Developer March 1, 2019 Mar 1, 2019 03/1/19

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.