Identifying meaningful feature interactions is a central challenge in building accurate and interpretable models for tabular data. Generalized additive models (GAMs) have shown great success at ...

Abstract: Generalized additive models (GAMs) have been successfully applied to high dimensional data. However, most existing methods cannot capture the high level feature patterns from complex data.

The era of size inclusivity is seemingly over. Our critic traces the shift and hopes designers might learn from it. By Vanessa Friedman I know models have always been skinny, but it seems to me they ...

Microsoft has added official Python support to Aspire 13, expanding the platform beyond .NET and JavaScript for building and running distributed apps. Documented today in a Microsoft DevBlogs post, ...

Vitamin D is a necessary nutrient that is important for calcium homeostasis and bone health. Dyslipidemia is thought to be a risk factor for the development of atherosclerotic illnesses. Recent ...

Among various vertical coordinate systems, the sigma coordinate—a terrain-following approach fitted to seabed topography—has been most widely used in coastal and continental shelf seas. However, it ...

Hello there! 👋 I'm Luca, a BI Developer with a passion for all things data, Proficient in Python, SQL and Power BI ...

Interpretability has drawn increasing attention in machine learning. Partially linear additive models provide an attractive middle ground between the simplicity of generalized linear model and the ...