xgboost alternatives and similar packages
Based on the "Machine Learning" category.
Alternatively, view xgboost alternatives based on common mentions on social networks and blogs.
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tensorflow
An Open Source Machine Learning Framework for Everyone -
gym
A toolkit for developing and comparing reinforcement learning algorithms. -
PaddlePaddle
PArallel Distributed Deep LEarning: Machine Learning Framework from Industrial Practice (『飞桨』核心框架,深度学习&机器学习高性能单机、分布式训练和跨平台部署) -
CNTK
Microsoft Cognitive Toolkit (CNTK), an open source deep-learning toolkit -
Prophet
Tool for producing high quality forecasts for time series data that has multiple seasonality with linear or non-linear growth. -
TFLearn
Deep learning library featuring a higher-level API for TensorFlow. -
NuPIC
Numenta Platform for Intelligent Computing is an implementation of Hierarchical Temporal Memory (HTM), a theory of intelligence based strictly on the neuroscience of the neocortex. -
H2O
H2O is an Open Source, Distributed, Fast & Scalable Machine Learning Platform: Deep Learning, Gradient Boosting (GBM) & XGBoost, Random Forest, Generalized Linear Modeling (GLM with Elastic Net), K-Means, PCA, Generalized Additive Models (GAM), RuleFit, Support Vector Machine (SVM), Stacked Ensembles, Automatic Machine Learning (AutoML), etc. -
Surprise
A Python scikit for building and analyzing recommender systems -
LightFM
A Python implementation of LightFM, a hybrid recommendation algorithm. -
Pylearn2
Warning: This project does not have any current developer. See bellow. -
skflow
Simplified interface for TensorFlow (mimicking Scikit Learn) for Deep Learning -
Sacred
Sacred is a tool to help you configure, organize, log and reproduce experiments developed at IDSIA. -
Clairvoyant
Software designed to identify and monitor social/historical cues for short term stock movement -
python-recsys
A python library for implementing a recommender system -
Metrics
Machine learning evaluation metrics, implemented in Python, R, Haskell, and MATLAB / Octave -
karateclub
Karate Club: An API Oriented Open-source Python Framework for Unsupervised Learning on Graphs (CIKM 2020) -
awesome-embedding-models
A curated list of awesome embedding models tutorials, projects and communities. -
Crab
Crab is a flexible, fast recommender engine for Python that integrates classic information filtering recommendation algorithms in the world of scientific Python packages (numpy, scipy, matplotlib). -
seqeval
A Python framework for sequence labeling evaluation(named-entity recognition, pos tagging, etc...) -
adaptive
:chart_with_upwards_trend: Adaptive: parallel active learning of mathematical functions -
Xorbits
Scalable Python DS & ML, in an API compatible & lightning fast way. -
TrueSkill, the video game rating system
An implementation of the TrueSkill rating system for Python -
SciKit-Learn Laboratory
SciKit-Learn Laboratory (SKLL) makes it easy to run machine learning experiments. -
rwa
Machine Learning on Sequential Data Using a Recurrent Weighted Average -
Feature Forge
A set of tools for creating and testing machine learning features, with a scikit-learn compatible API -
Data Flow Facilitator for Machine Learning (dffml)
The easiest way to use Machine Learning. Mix and match underlying ML libraries and data set sources. Generate new datasets or modify existing ones with ease. -
brew
Multiple Classifier Systems and Ensemble Learning Library in Python. -
Robocorp Action Server
Create 🐍 Python AI Actions and 🤖 Automations, and deploy & operate them anywhere -
MLP Classifier
A handwritten multilayer perceptron classifer using numpy. -
OptaPy
OptaPy is an AI constraint solver for Python to optimize planning and scheduling problems. -
omega-ml
MLOps simplified. From ML Pipeline ⇨ Data Product without the hassle -
ChaiPy
A developer interface for creating advanced chatbots for the Chai app. -
tfgraphviz
A visualization tool to show a TensorFlow's graph like TensorBoard
InfluxDB - Power Real-Time Data Analytics at Scale
* Code Quality Rankings and insights are calculated and provided by Lumnify.
They vary from L1 to L5 with "L5" being the highest.
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README
eXtreme Gradient Boosting
Community | Documentation | [Resources](demo/README.md) | [Contributors](CONTRIBUTORS.md) | [Release Notes](NEWS.md)
XGBoost is an optimized distributed gradient boosting library designed to be highly efficient, flexible and portable. It implements machine learning algorithms under the Gradient Boosting framework. XGBoost provides a parallel tree boosting (also known as GBDT, GBM) that solve many data science problems in a fast and accurate way. The same code runs on major distributed environment (Kubernetes, Hadoop, SGE, MPI, Dask) and can solve problems beyond billions of examples.
License
© Contributors, 2021. Licensed under an Apache-2 license.
Contribute to XGBoost
XGBoost has been developed and used by a group of active community members. Your help is very valuable to make the package better for everyone. Checkout the Community Page.
Reference
- Tianqi Chen and Carlos Guestrin. XGBoost: A Scalable Tree Boosting System. In 22nd SIGKDD Conference on Knowledge Discovery and Data Mining, 2016
- XGBoost originates from research project at University of Washington.
Sponsors
Become a sponsor and get a logo here. See details at Sponsoring the XGBoost Project. The funds are used to defray the cost of continuous integration and testing infrastructure (https://xgboost-ci.net).
Open Source Collective sponsors
Sponsors
Backers
*Note that all licence references and agreements mentioned in the xgboost README section above
are relevant to that project's source code only.