google/yggdrasil-decision-forests

A fast, scalable, high performance Gradient Boosting on Decision Trees library, used for ranking, classification, regression and other machine learning tasks for Python, R, Java, C++. Supports computation on CPU and GPU.

A fast, distributed, high performance gradient boosting (GBT, GBDT, GBRT, GBM or MART) framework based on decision tree algorithms, used for ranking, classification and many other machine learning tasks.

✨ Build a machine learning model from a prompt

Lazy Predict help build a lot of basic models without much code and helps understand which models works better without any parameter tuning

Interpretable ML package 🔍 for concise, transparent, and accurate predictive modeling (sklearn-compatible).

Fit interpretable models. Explain blackbox machine learning.

TabFM (Tabular Foundation Model) is a pretrained tabular foundation model developed by Google Research for tabular data regression and classification.

⚡ TabPFN: Foundation Model for Tabular Data ⚡

Impress your boss with interactive Decision Tree visualization