From 7d1c5c8d0083f6ed23bdff7f00ed8ca24a7c3cc6 Mon Sep 17 00:00:00 2001 From: Vinta Chen Date: Sun, 16 Aug 2026 14:48:21 +0800 Subject: [PATCH] audit: sweep Machine Learning, three-way split, drop h2o, mindsdb, scikit-lego, TabGAN, spark.ml MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Restructure: the 12-entry flat section splits into General (scikit-learn 234.7M/mo obvious choice; pgmpy 843.7K/mo and feature-engine — renamed from feature_engine to its canonical PyPI name, 297.1K/mo — challengers), Gradient Boosting (xgboost 52M/mo, lightgbm 26.5M/mo, catboost 6.3M/mo, all obvious choices; lightgbm's lightgbm-org link verified current — microsoft/LightGBM redirects there), and Time Series Forecasting (timesfm sole — a foundation model judged by ecosystem adoption, 285K/mo and 27.6K stars; prophet and darts are named absences, deliberately not added this sitting). Removed: - h2o — 215.1K downloads/month, 7.5K stars, and the repo is active; the drop is purely editorial: no longer anyone's unprompted answer against scikit-learn and the boosting trio. Judgment call. - mindsdb — the linked repo redirects to mindsdb/mindshub, a "models workspace"; the AI-layer-for-databases product this entry described no longer exists (verified). 23.9K downloads/month. - scikit-lego — 72.5K downloads/month, 1.4K stars; a grab-bag of sklearn extras that never became an unprompted answer. Judgment. - TabGAN — 574 stars, 2.3K downloads/month. Nowhere near the bar. - spark.ml — duplicate in all but name: pyspark is already listed in the audited DevOps group, same repo, same pip install. Structural. Co-Authored-By: Claude --- README.md | 22 ++++++++++------------ 1 file changed, 10 insertions(+), 12 deletions(-) diff --git a/README.md b/README.md index add6a1f4..1cc12b1d 100644 --- a/README.md +++ b/README.md @@ -191,18 +191,16 @@ _Frameworks for Neural Networks and Deep Learning. Also see [awesome-deep-learni _Libraries for Machine Learning. Also see [awesome-machine-learning](https://github.com/josephmisiti/awesome-machine-learning#python)._ -- [catboost](https://github.com/catboost/catboost) - A fast, scalable, high performance gradient boosting on decision trees library. -- [feature_engine](https://github.com/feature-engine/feature_engine) - sklearn compatible API with the widest toolset for feature engineering and selection. -- [h2o](https://github.com/h2oai/h2o-3) - Open Source Fast Scalable Machine Learning Platform. -- [lightgbm](https://github.com/lightgbm-org/LightGBM) - A fast, distributed, high performance gradient boosting framework. -- [mindsdb](https://github.com/mindsdb/minds) - MindsDB is an open source AI layer for existing databases that allows you to effortlessly develop, train and deploy state-of-the-art machine learning models using standard queries. -- [pgmpy](https://github.com/pgmpy/pgmpy) - A Python library for probabilistic graphical models and Bayesian networks. -- [scikit-learn](https://github.com/scikit-learn/scikit-learn) - The most popular Python library for Machine Learning with extensive documentation and community support. -- [scikit-lego](https://github.com/koaning/scikit-lego) - A collection of lego bricks for scikit-learn pipelines. -- [spark.ml](https://github.com/apache/spark) - [Apache Spark](https://spark.apache.org/)'s scalable [Machine Learning library](https://spark.apache.org/docs/latest/ml-guide.html) for distributed computing. -- [TabGAN](https://github.com/Diyago/Tabular-data-generation) - Synthetic tabular data generation using GANs, Diffusion Models, and LLMs. -- [timesfm](https://github.com/google-research/timesfm) - A pretrained foundation model from Google Research for time-series forecasting. -- [xgboost](https://github.com/dmlc/xgboost) - A scalable, portable, and distributed gradient boosting library. +- General + - [scikit-learn](https://github.com/scikit-learn/scikit-learn) - The most popular Python library for Machine Learning with extensive documentation and community support. + - [pgmpy](https://github.com/pgmpy/pgmpy) - A Python library for probabilistic graphical models and Bayesian networks. + - [feature-engine](https://github.com/feature-engine/feature_engine) - sklearn compatible API with the widest toolset for feature engineering and selection. +- Gradient Boosting + - [xgboost](https://github.com/dmlc/xgboost) - A scalable, portable, and distributed gradient boosting library. + - [lightgbm](https://github.com/lightgbm-org/LightGBM) - A fast, distributed, high performance gradient boosting framework. + - [catboost](https://github.com/catboost/catboost) - A fast, scalable, high performance gradient boosting on decision trees library. +- Time Series Forecasting + - [timesfm](https://github.com/google-research/timesfm) - A pretrained foundation model from Google Research for time-series forecasting. ### Natural Language Processing