Most of the content of this post is platform-agnostic. Since in these days I’m using Azure Machine Learning, I take it as a starting point of my studies.
It’s quite simple for an Azure Machine Learning average user to create a regression experiment, make the data flow in it and get the predicted values. It’s also easy to have some metrics to evaluate the implemented model. Once you get them, the following questions arise:
- How can I interpret these numbers?
- Are these metrics enough to assess the goodness-of-fit of the model?
This post wants to provide you with the statistical foundation behind these metrics and with some additional tools that will help you to better understand how the model has fitted. These tools are implemented in a R script you can simply copy&paste into an Execute R Script module.
Read the rest of the article here:
Latest posts by Luca Zavarella (see all)
- How to Better Evaluate the Goodness-of-Fit of Regressions - September 13, 2017
- How to bulk copy Azure ML Experiments from a Workspace to another one or do a Backup of them in Physical Files - February 6, 2017
- Fixed some SQL Server Partition Management Utility Bugs - November 10, 2015