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Multiple Pairwise Comparisons (Post Hoc) Tests in Python
The main features of maximtrp/scikit-posthocs are: General Machine Learning, Machine Learning Frameworks, Machine Learning Packages, Statistical Analysis, Statistics, Statistical Modeling.
Projects with overlapping indexed features include: dswah/pygam — [CONTRIBUTORS WELCOME] Generalized Additive Models in Python. danielhanchen/hyperlearn — 2-2000x faster ML algos, 50% less memory usage, works on all hardware - new and old. aksnzhy/xlearn — High performance, easy-to-use, and scalable machine learning (ML) package, including linear model (LR), factorization… amazaspshumik/sklearn-bayes — Python package for Bayesian Machine Learning with scikit-learn API. csinva/imodels — Interpretable ML package 🔍 for concise, transparent, and accurate predictive modeling (sklearn-compatible). christophm/rulefit — Python implementation of the rulefit algorithm.
CONTRIBUTORS WELCOME Generalized Additive Models in Python
High performance, easy-to-use, and scalable machine learning (ML) package, including linear model (LR), factorization machines (FM), and field-aware factorization machines (FFM) for Python and CLI interface.
Python package for Bayesian Machine Learning with scikit-learn API
