Xgboost Python Packages

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

xgboost-cpu

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

mlforecast

Scalable machine 🤖 learning for time series forecasting.

xgboost-ray

Distributed XGBoost on Ray

neptune

📘 The experiment tracker for foundation model training

kserve

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

skforecast

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

dtreeviz

A python library for decision tree visualization and model interpretation.

neptune-client

📘 The experiment tracker for foundation model training

predikit

The missing bridge between your ML models and your AI agents.

kubeflow-training

Distributed AI Model Training and LLM Fine-Tuning on Kubernetes

fasttreeshap

Fast SHAP value computation for interpreting tree-based models

autoviz

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

pymars

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

kubeflow-trainer-api

Distributed AI Model Training and LLM Fine-Tuning on Kubernetes

m2cgen

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

mljar-supervised

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

shap-hypetune

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

nyoka

Nyoka is a Python library that helps to export ML models into PMML (PMML 4.4.1 Standard).

hyperactive

A unified interface for optimization algorithms and experiments

supermodelingfactory

Production-grade Python toolkit for credit risk modeling — scorecards, LR, LightGBM/XGBoost, WOE/IV, PSI, KS, SHAP. Source-protected wheels via Cython.

featurewiz

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.

rhea

ML/AI training/serving and agent sandbox operator and controller for Kubernetes

hgboost

hgboost is a python package for hyper-parameter optimization for xgboost, catboost or lightboost using cross-validation, and evaluating the results on an independent validation set. hgboost can be applied for classification and regression tasks.