We discuss a new approach for selecting features from a large set of features, in an unsupervised machine learning framework. In supervised learning such as linear regression or supervised clustering, it is possible to test the predicting power of a set of features (also called independent variables by statisticians, or predictors) using metrics such as goodness of fit with the response (the dependent variable), for instance using the R-squared coefficient. This makes the process of feature…

Here are our selection of featured contributions posted in the last few days:

Featured Resources and Technical Contributions

• AutoEncoders and Parallel Computing with KernelML
• New Book: Mastering Machine Learning…

Python continues to take leading positions in solving data science tasks and challenges. Last year we made a blog post overviewing the Python’s libraries that proved to be the most helpful at that moment. This year, we expanded our list with new libraries and gave a fresh look to the ones we already talked about, focusing on the updates that have been made during the year.…

The impact of a change of scale, for instance using years instead of days as the unit of measurement for one variable in a clustering problem, can be dramatic. It can result in a totally different cluster structure. Frequently, this is not a desirable property, yet it is rarely mentioned in textbooks. I think all clustering software should state in their user guide, that the algorithm is sensitive to scale.

We illustrate the problem here, and propose a scale-invariant methodology for…

New article by Bill Vorhies.

Summary:  There is a great hue and cry about the danger of bias in our predictive models when applied to high significance events like who gets a loan, insurance, a good school assignment, or bail.  It’s not as simple as it seems and here we try to take a more nuanced look.  The result is not as threatening as many headlines make it seem.…