xgboost

Scalable, Portable and Distributed Gradient Boosting (GBDT, GBRT or GBM) Library, for Python, R, Java, Scala, C and more. Runs on single machine, Hadoop, Spark, Dask, Flink and DataFlow

Standardized Distributed Generative and Predictive AI Inference Platform for Scalable, Multi-Framework Deployment on Kubernetes

Alink is the Machine Learning algorithm platform based on Flink, developed by the PAI team of Alibaba computing platform.

Python package for AutoML on Tabular Data with Feature Engineering, Hyper-Parameters Tuning, Explanations and Automatic Documentation

A python library for decision tree visualization and model interpretation.

Transform ML models into a native code (Java, C, Python, Go, JavaScript, Visual Basic, C#, R, PowerShell, PHP, Dart, Haskell, Ruby, F#, Rust) with zero dependencies

A library for debugging/inspecting machine learning classifiers and explaining their predictions

Mars is a tensor-based unified framework for large-scale data computation which scales numpy, pandas, scikit-learn and Python functions.

Deep Learning Server and CLI for Torch and TensorRT

A collection of research papers on decision, classification and regression trees with implementations.

Distributed AI Model Training and LLM Fine-Tuning on Kubernetes

Automatically Visualize any dataset, any size with a single line of code. Created by Ram Seshadri. Collaborators Welcome. Permission Granted upon Request.

Python library for time series forecasting using scikit-learn compatible models, statistical methods, and foundation models

Scalable machine 🤖 learning for time series forecasting.

Scalable Python DS & ML, in an API compatible & lightning fast way.

A curated list of gradient boosting research papers with implementations.

An inference server for your machine learning models, including support for multiple frameworks, multi-model serving and more

An extension of XGBoost to probabilistic modelling

Ollama for classical ML models. AOT compiler that turns XGBoost, LightGBM, scikit-learn, CatBoost & ONNX models into native C99 inference code. One command to load, one command to serve. 336x faster than Python inference.

Use advanced feature engineering strategies and select best features from your data set with a single line of code. Created by Ram Seshadri. Collaborators welcome.

Impress your boss with interactive Decision Tree visualization

Code for IDS-ML: intrusion detection system development using machine learning algorithms (Decision tree, random forest, extra trees, XGBoost, stacking, k-means, Bayesian optimization..)

REST web service for the true real-time scoring (<1 ms) of Scikit-Learn, R and Apache Spark models

A python package for simultaneous Hyperparameters Tuning and Features Selection for Gradient Boosting Models.

A unified interface for optimization algorithms and experiments

Automatically Build Multiple ML Models with a Single Line of Code. Created by Ram Seshadri. Collaborators Welcome. Permission Granted upon Request.