catboost/benchmarks

scikit-learn_bench benchmarks various implementations of machine learning algorithms across data analytics frameworks. It currently support the scikit-learn, DAAL4PY, cuML, and XGBoost frameworks for commonly used machine learning algorithms.

The repository to showcase the best framework for tabular data - the Awesome CatBoost

CatBoost tutorials repository

A suite to benchmark CPU/GPU Python performance in training ML models and running local LLMs

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.

🤗 Evaluate: A library for easily evaluating machine learning models and datasets.

Python AutoML toolkit for benchmarking classification, regression, and time-series forecasting models, with GPU acceleration, MLflow tracking, and automated preprocessing.

A Living Benchmark for Machine Learning on Tabular Data

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.