Himalaya [1] implements machine learning linear models in Python, focusing on computational efficiency for large numbers of targets. Use himalaya if you need a library that: estimates linear models on ...

Firth penalization reduces small-sample bias and produces finite estimates even when standard MLE fails due to (quasi-)complete separation or monotone likelihood. Standard maximum-likelihood logistic ...

Scientists have long sought to derive models from extensive observational input–output data, ensuring these models accurately capture the underlying mapping from inputs to outputs while remaining ...

The right Python libraries can dramatically improve speed, efficiency, and maintainability in 2025 projects. Mastering a mix of data, AI, and web-focused libraries ensures adaptability across multiple ...

Machine learning and deep learning have been widely embraced, and even more widely misunderstood. In this article, I’ll step back and explain both machine learning and deep learning in basic terms, ...

Geothermal heat flow (GHF) data measured directly from boreholes are sparse. Purely physics-based models for geothermal heat flow prediction require various simplifications and are feasible only for ...

Given the ubiquity of amide coupling reactions, understanding the factors which influence the success of the reaction and having means to predict the reaction rate would streamline synthetic efforts.