hyperlearn

Why we included this project

Most ML workloads never touch a neural network, and for those, hyperlearn is a practical speed upgrade. It reimplements familiar estimators like linear and ridge regression, PCA, matrix factorization, and clustering, with the heavy math rewritten in Cython, C++, and assembly so the same fits run far faster and use less memory. For teams running repeated fits on wide tabular data, that can mean shorter batch jobs and cheaper compute. The API stays close to scikit-learn's conventions, so existing modeling code can often be swapped in without a rewrite, and it adds statistical inference helpers like confidence intervals, hypothesis tests, and goodness-of-fit measures that statsmodels users will recognize. One caveat: this is a small, community-maintained project with a handful of maintainers, so treat it as an acceleration layer for well-understood classical models rather than a full replacement for the scikit-learn ecosystem, and validate results against your existing baseline before relying on it in production.