Index of Packages Matching 'machine learning'
| Package | Weight* | Description |
|---|---|---|
| machineLearningStanford 0.0 | 14 | Machine Learning Stanford |
| costcla 0.4 | 10 | costcla is a Python module for cost-sensitive machine learning (classification) |
| deeplearning 0.0.1 | 10 | Deep learning framework in Python |
| dlib 18.17.99 | 10 | A toolkit for making real world machine learning and data analysis applications |
| dlib 18.17.100 | 10 | A toolkit for making real world machine learning and data analysis applications |
| elm 0.1.1 | 10 | Python Extreme Learning Machine (ELM) is a machine learning technique used for classification/regression tasks. |
| featureforge 0.1.6 | 10 | A library to build and test machine learning features |
| h2o 3.0.1.4 | 10 | H2O, Fast Scalable Machine Learning, for python |
| h2o 3.2.0.1 | 10 | H2O, Fast Scalable Machine Learning, for python |
| h2o 3.2.0.3 | 10 | H2O, Fast Scalable Machine Learning, for python |
| h2o 3.2.0.5 | 10 | H2O, Fast Scalable Machine Learning, for python |
| h2o 3.2.0.8 | 10 | H2O, Fast Scalable Machine Learning, for python |
| h2o 3.2.0.9 | 10 | H2O, Fast Scalable Machine Learning, for python |
| h2o 3.6.0.3 | 10 | H2O, Fast Scalable Machine Learning, for python |
| h2o 3.6.0.8 | 10 | H2O, Fast Scalable Machine Learning, for python |
| hep_ml 0.3.0 | 10 | Machine Learning for High Energy Physics |
| hep_ml 0.4.0 | 10 | Machine Learning for High Energy Physics |
| LearningRobot 0.0.0.dev0 | 10 | Robotics-related Probabilistic Reasoning & Machine Learning |
| malss 0.5.1 | 10 | MALSS: MAchine Learning Support System |
| mlpy 0.1.0 | 10 | A machine learning library for Python |
| Monte 0.0.11 | 10 | Monte - machine learning in pure Python. |
| oll 0.1.2 | 10 | Online machine learning algorithms library (wrapper for OLL C++ library) |
| Pattern 2.6 | 10 | Web mining module for Python, with tools for scraping, natural language processing, machine learning, network analysis and visualization. |
| pymlx 0.0.8 | 10 | Yet another machine learning framework |
| pysterior 0.1.1 | 10 | Bayesian machine learning in Python. |
| pysterior 0.1.2 | 10 | Bayesian machine learning in Python. |
| pysterior 0.1.3 | 10 | Bayesian machine learning in Python. |
| pysterior 0.1.4 | 10 | Bayesian machine learning in Python. |
| pysterior 0.1.5 | 10 | Bayesian machine learning in Python. |
| ramp 0.1.4 | 10 | Rapid machine learning prototyping |
| upsilon 1.2.0 | 10 | Automated Classification of Periodic Variable Stars Using Machine Learning |
| upsilon 1.2.1 | 10 | Automated Classification of Periodic Variable Stars Using Machine Learning |
| apsis 0.1.1 | 9 | Toolkit for hyperparameter optimization for machine learning algorithms. |
| edxclassify 0.10.a1 | 9 | A machine learning workflow with classifiers to detect affect in MOOC discussion forums. |
| Reinforcement-Learning-Toolkit 1.0 | 9 | |
| digipy 0.1.1 | 8 | a cool demo for Montreal Python 6 to do real time digits recognition using Machine Learning and good Features |
| ease 0.1.1 | 8 | Machine learning based automated text classification library. Useful for essay scoring and other tasks. Please see https://github.com/edx/discern for an API wrapper of this code. |
| lmj.rbm 0.1.1 | 8 | A library of Restricted Boltzmann Machines |
| Lurrn 0.7.2.1 | 8 | Simple machine learning library |
| MLx 0.0.1 | 8 | Yet another machine learning library |
| pcSVM pre 1.0 | 8 | pcSVM is a framework for support vector machines |
| percept 0.14 | 8 | Modular machine learning framework that is easy to test and deploy. |
| pyrouette 0.6.0 | 8 | A pythonic machine learning library |
| pystacks 0.3 | 8 | Python library for hierarchical machine learning |
| skll 1.1.0 | 8 | SciKit-Learn Laboratory makes it easier to run machinelearning experiments with scikit-learn. |
| skll 1.1.1 | 8 | SciKit-Learn Laboratory makes it easier to run machinelearning experiments with scikit-learn. |
| tictacs 0.0.1 | 8 | Machine learning pipeline creation from config files |
| tictacs 0.0.2 | 8 | Machine learning pipeline creation from config files |
| twistml 0.1 | 8 | TWItter STock market Machine Learning package |
| twistml 0.1.1 | 8 | TWItter STock market Machine Learning package |
| twistml 0.1.2 | 8 | TWItter STock market Machine Learning package |
| mlutils 0.1.0b | 7 | Collection of utilities for AI planning and not-supervised learning. Development is in progress. |
| pcSVMdemo 1.0 | 7 | pcSVMdemo demonstrates the operating principles of support vector machines (SMVs) |
| pescador 0.1.0 | 7 | Multiplex generators for incremental learning |
| abstraction 2015.10.30.2039 | 6 | machine learning framework |
| abstraction 2015.12.2.1425 | 6 | machine learning framework |
| am2 0.1 | 6 | Stuff made on the machine learning course at my university |
| astroML 0.3 | 6 | tools for machine learning and data mining in Astronomy |
| autocomplete 0.0.104 | 6 | tiny 'autocomplete' tool using a "hidden markov model" |
| bob 2.0.5 | 6 | Bob is a free signal-processing and machine learning toolbox originally developed by the Biometrics group at Idiap Research Institute, in Switzerland. |
| bob 2.0.6 | 6 | Bob is a free signal-processing and machine learning toolbox originally developed by the Biometrics group at Idiap Research Institute, in Switzerland. |
| Cluster_Ensembles 1.15 | 4 | A package for determining the consensus clustering from an ensemble of partitions |
| Concurrent_AP 1.1 | 6 | Scalable and parallel programming implementation of Affinity Propagation clustering |
| Concurrent_AP 1.2 | 6 | Scalable and parallel programming implementation of Affinity Propagation clustering |
| Concurrent_AP 1.3 | 6 | Scalable and parallel programming implementation of Affinity Propagation clustering |
| DBSCAN_multiplex 1.1 | 6 | Fast and memory-efficient DBSCAN clustering,possibly on various subsamples out of a common dataset |
| DBSCAN_multiplex 1.3 | 6 | Fast and memory-efficient DBSCAN clustering,possibly on various subsamples out of a common dataset |
| DBSCAN_multiplex 1.5 | 6 | Fast and memory-efficient DBSCAN clustering,possibly on various subsamples out of a common dataset |
| DBSCAN_multiplex 1.4 | 6 | Fast and memory-efficient DBSCAN clustering,possibly on various subsamples out of a common dataset |
| dreaml 0.0.1 | 4 | dreaml (dynamic reactive machine learning) |
| eatiht 0.1.14 | 6 | A simple tool used to extract an article's text in html documents. |
| formasaurus 0.5 | 2 | Formasaurus tells you the types of HTML forms and their fields using machine learning |
| frontier 0.1.2 | 6 | Provides interfaces for the reading, storage and retrieval of large machine learning data sets for use with scikit-learn |
| GPy 0.9.1 | 2 | The Gaussian Process Toolbox |
| GPy 0.8.30 | 2 | The Gaussian Process Toolbox |
| GPyOpt 0.1.4 | 6 | The Bayesian Optimization Toolbox |
| hpelm 0.6.21 | 6 | High-Performance implementation of an Extreme Learning Machine |
| hpelm 0.6.22 | 6 | High-Performance implementation of an Extreme Learning Machine |
| ibmdbpy 0.1.0b11 | 6 | A Pandas-like SQL-wrapper for in-database analytics with IBM dashDB/DB2. |
| ibmdbpy 0.1.0b6 | 6 | A Pandas-like SQL-wrapper for in-database analytics with IBM dashDB/DB2. |
| ibmdbpy 0.1.0b7 | 6 | A Pandas-like SQL-wrapper for in-database analytics with IBM dashDB/DB2. |
| ibmdbpy 0.1.0b8 | 6 | A Pandas-like SQL-wrapper for in-database analytics with IBM dashDB/DB2. |
| ibmdbpy 0.1.0b12 | 6 | A Pandas-like SQL-wrapper for in-database analytics with IBM dashDB/DB2. |
| ibmdbpy 0.1.0b5 | 6 | A Pandas-like SQL-wrapper for in-database analytics with IBM dashDB/DB2. |
| ibmdbpy 0.1.0b4 | 6 | A Pandas-like SQL-wrapper for in-database analytics with IBM dashDB/DB2. |
| ibmdbpy 0.1.0b17 | 6 | A Pandas-like SQL-wrapper for in-database analytics with IBM dashDB/DB2. |
| ibmdbpy 0.1.0b15 | 6 | A Pandas-like SQL-wrapper for in-database analytics with IBM dashDB/DB2. |
| ibmdbpy 0.1.0b14 | 6 | A Pandas-like SQL-wrapper for in-database analytics with IBM dashDB/DB2. |
| ibmdbpy 0.1.0b20 | 6 | A Pandas-like SQL-wrapper for in-database analytics with IBM dashDB/DB2. |
| ibmdbpy 0.1.0b21 | 6 | A Pandas-like SQL-wrapper for in-database analytics with IBM dashDB/DB2. |
| ibmdbpy 0.1.0b19 | 6 | A Pandas-like SQL-wrapper for in-database analytics with IBM dashDB/DB2. |
| ibmdbpy 0.1.0b16 | 6 | A Pandas-like SQL-wrapper for in-database analytics with IBM dashDB/DB2. |
| infer 0.1 | 6 | A machine learning toolkit for classification and assisted experimentation. |
| lspi-python 1.0.1 | 6 | LSPI algorithm in Python |
| medlearn 0.0.1 | 6 | Understand medical school admissions with machine learning |
| mempamal 0.1.5 | 6 | MEMPAMAL: Means for EMbarrassingly PArallel MAchine Learning |
| metaopt 0.1.0 | 6 | MetaOpt is a library that optimizes black-box functions using a limited amount of time and utilizing multiple processors. The main focus of MetaOpt is the parameter tuning for machine learning and heuristic optimization. |
| milk 0.6.1 | 6 | Machine Learning Toolkit |
| milksets 0.2 | 6 | Milk sets: Machine Learning Datasets |
| MLizard 0.1.2 | 6 | Machine Learning workflow automatization |
| mltsp 0.3.1 | 6 | Machine Learning Time-Series Platform |
| mltsp 0.2.3 | 6 | Machine Learning Time-Series Platform |
| mltsp 0.3.0 | 6 | Machine Learning Time-Series Platform |
| mltsp 0.3.2 | 6 | Machine Learning Time-Series Platform |
| mltsp 0.3.3 | 6 | Machine Learning Time-Series Platform |
| mmlf 1.0 | 6 | The Maja Machine Learning Framework |
| neural 0.1.0 | 6 | Simple neural network implementation in Python based on Andrew Ng's Machine Learning online course. |
| nlp 0.0.1 | 6 | Deep learning framework in Python |
| Optunity 1.1.1 | 6 | Optimization routines for hyperparameter tuning. |
| Optunity 1.1.0 | 6 | Optimization routines for hyperparameter tuning. |
| Orange3-spark 0.1.9 | 6 | A series of Widgets for Orange3 to work with Spark ML |
| Orange3-spark 0.1.8 | 6 | A series of Widgets for Orange3 to work with Spark ML |
| Orange3-spark 0.1.7 | 6 | A series of Widgets for Orange3 to work with Spark ML |
| orangecontrib.earth 0.1.3 | 6 | An implementation of MARS algorithm for Orange. |
| patent-parsing-tools 0.9 | 6 | patent-parsing-tools is a library providing tools for generating training and test set from Google's USPTO data helpful with for testing machine learning algorithms |
| patent-parsing-tools 0.9.1 | 6 | patent-parsing-tools is a library providing tools for generating training and test set from Google's USPTO data helpful with for testing machine learning algorithms |
| Peach 0.3.1 | 6 | Python library for computational intelligence and machine learning |
| plugml 0.2.1 | 6 | easy-to-use and highly modular machine learning framework |
| pmll 0.2.2 | 6 | Python machine learning library |
| prettytensor 0.5.1 | 6 | Pretty Tensor makes learning beautiful |
| prettytensor 0.5.0 | 6 | Pretty Tensor makes learning beautiful |
| pug-invest 0.0.18 | 6 | # pug-invest |
| PyAI 2.12 | 6 | Python Machine Learning Framework |
| PyBrain2 0.4.0 | 6 | PyBrain2 is the modestly improved PyBrain, the Swiss army knife for neural networking. |
| pyimpute 0.1 | 6 | Utilities for applying scikit-learn to spatial datasets |
| pyimpute 0.0.3 | 6 | Utilities for applying scikit-learn to spatial datasets |
| pyimpute 0.1.2 | 6 | Utilities for applying scikit-learn to spatial datasets |
| pymadlib 0.1.7 | 6 | A Python wrapper for MADlib (http://madlib.net) - an open source library for scalable in-database machine learning algorithms |
| pymldb 0.4.1 | 6 | Python interface to MLDB |
| pymldb 0.3.2 | 6 | Python interface to MLDB |
| pyrallel 0.2.1 | 6 | Experimental tools for parallel machine learning |
| pywFM 0.3 | 4 | pywFM is a Python wrapper for Steffen Rendle's factorization machines library libFM |
| redditnlp 0.1.3 | 6 | A tool to perform natural language processing of reddit content. |
| scikit-learn 0.16.1 | 6 | A set of python modules for machine learning and data mining |
| scikit-learn 0.17b1 | 6 | A set of python modules for machine learning and data mining |
| scikit-learn 0.17 | 6 | A set of python modules for machine learning and data mining |
| scikits.learn 0.8.1 | 6 | A set of python modules for machine learning and data mining |
| skdata 0.0.4 | 6 | Data Sets for Machine Learning in Python |
| sklearn-extensions 0.0.2 | 6 | A bundle of 3rd party extensions to scikit-learn |
| sklearn-extensions 0.0.1 | 6 | A bundle of 3rd party extensions to scikit-learn |
| theanets 0.6.2 | 6 | A library of neural nets in theano |
| theanets 0.7.0 | 6 | Feedforward and recurrent neural nets using Theano |
| theanets 0.7.2 | 6 | Feedforward and recurrent neural nets using Theano |
| theanets 0.7.1 | 6 | Feedforward and recurrent neural nets using Theano |
| TPOT 0.2.0 | 6 | Tree-based Pipeline Optimization Tool |
| TPOT 0.1.3 | 6 | Tree-based Pipeline Optimization Tool |
| TPOT 0.1.2 | 6 | Tree-based Pipeline Optimization Tool |
| TPOT 0.1.1 | 6 | Tree-based Pipeline Optimization Tool |
| tradingmachine 0.1.7 | 6 | A backtester for financial algorithms. |
| vsmrfs 0.9.0 | 6 | Vector-Space Markov Random Fields |
| wabbit_wappa 0.3.0 | 6 | Wabbit Wappa is a full-featured Python wrapper for the Vowpal Wabbit machine learning utility. |
| whetlab 0.2.3.11 | 6 | Whetlab client for Python |
| wise 0.9.8 | 6 | Client library for the wise.io machine-learning service. |
| xbob.paper.tpami2013 1.0.0 | 6 | Example on how to use the scalable implementation of PLDA and how to reproduce experiments of the article |
| xcs 1.0.0 | 6 | XCS (Accuracy-based Classifier System) |
| xgboost 0.4a14 | 6 | <img src=https://raw.githubusercontent.com/dmlc/dmlc.github.io/master/img/logo-m/xgboost.png width=135/> eXtreme Gradient Boosting =========== [](https://travis-ci.org/dmlc/xgboost) [](https://xgboost.readthedocs.org) [](http://cran.r-project.org/web/packages/xgboost) [](https://gitter.im/dmlc/xgboost?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge&utm_content=badge) An optimized general purpose gradient boosting library. The library is parallelized, and also provides an optimized distributed version. It implements machine learning algorithms under the [Gradient Boosting](https://en.wikipedia.org/wiki/Gradient_boosting) framework, including [Generalized Linear Model](https://en.wikipedia.org/wiki/Generalized_linear_model) (GLM) and [Gradient Boosted Decision Trees](https://en.wikipedia.org/wiki/Gradient_boosting#Gradient_tree_boosting) (GBDT). XGBoost can also be [distributed](#features) and scale to Terascale data XGBoost is part of [Distributed Machine Learning Common](http://dmlc.github.io/) <img src=https://avatars2.githubusercontent.com/u/11508361?v=3&s=20> projects Contents -------- * [What's New](#whats-new) * [Version](#version) * [Documentation](doc/index.md) * [Build Instruction](doc/build.md) * [Features](#features) * [Distributed XGBoost](multi-node) * [Usecases](doc/index.md#highlight-links) * [Bug Reporting](#bug-reporting) * [Contributing to XGBoost](#contributing-to-xgboost) * [Committers and Contributors](CONTRIBUTORS.md) * [License](#license) * [XGBoost in Graphlab Create](#xgboost-in-graphlab-create) What's New ---------- * XGBoost helps Owen Zhang to win the [Avito Context Ad Click competition](https://www.kaggle.com/c/avito-context-ad-clicks). Check out the [interview from Kaggle](http://blog.kaggle.com/2015/08/26/avito-winners-interview-1st-place-owen-zhang/). * XGBoost helps Chenglong Chen to win [Kaggle CrowdFlower Competition](https://www.kaggle.com/c/crowdflower-search-relevance) Check out the [winning solution](https://github.com/ChenglongChen/Kaggle_CrowdFlower) * XGBoost-0.4 release, see [CHANGES.md](CHANGES.md#xgboost-04) * XGBoost helps three champion teams to win [WWW2015 Microsoft Malware Classification Challenge (BIG 2015)](http://www.kaggle.com/c/malware-classification/forums/t/13490/say-no-to-overfitting-approaches-sharing) Check out the [winning solution](doc/README.md#highlight-links) * [External Memory Version](doc/external_memory.md) Version ------- * Current version xgboost-0.4 - [Change log](CHANGES.md) - This version is compatible with 0.3x versions Features -------- * Easily accessible through CLI, [python](https://github.com/dmlc/xgboost/blob/master/demo/guide-python/basic_walkthrough.py), [R](https://github.com/dmlc/xgboost/blob/master/R-package/demo/basic_walkthrough.R), [Julia](https://github.com/antinucleon/XGBoost.jl/blob/master/demo/basic_walkthrough.jl) * Its fast! Benchmark numbers comparing xgboost, H20, Spark, R - [benchm-ml numbers](https://github.com/szilard/benchm-ml) * Memory efficient - Handles sparse matrices, supports external memory * Accurate prediction, and used extensively by data scientists and kagglers - [highlight links](https://github.com/dmlc/xgboost/blob/master/doc/README.md#highlight-links) * Distributed version runs on Hadoop (YARN), MPI, SGE etc., scales to billions of examples. Bug Reporting ------------- * For reporting bugs please use the [xgboost/issues](https://github.com/dmlc/xgboost/issues) page. * For generic questions or to share your experience using xgboost please use the [XGBoost User Group](https://groups.google.com/forum/#!forum/xgboost-user/) Contributing to XGBoost ----------------------- XGBoost has been developed and used by a group of active community members. Everyone is more than welcome to contribute. It is a way to make the project better and more accessible to more users. * Check out [Feature Wish List](https://github.com/dmlc/xgboost/labels/Wish-List) to see what can be improved, or open an issue if you want something. * Contribute to the [documents and examples](https://github.com/dmlc/xgboost/blob/master/doc/) to share your experience with other users. * Please add your name to [CONTRIBUTORS.md](CONTRIBUTORS.md) after your patch has been merged. License ------- © Contributors, 2015. Licensed under an [Apache-2](https://github.com/dmlc/xgboost/blob/master/LICENSE) license. XGBoost in Graphlab Create -------------------------- * XGBoost is adopted as part of boosted tree toolkit in Graphlab Create (GLC). Graphlab Create is a powerful python toolkit that allows you to do data manipulation, graph processing, hyper-parameter search, and visualization of TeraBytes scale data in one framework. Try the [Graphlab Create](http://graphlab.com/products/create/quick-start-guide.html) * Nice [blogpost](http://blog.graphlab.com/using-gradient-boosted-trees-to-predict-bike-sharing-demand) by Jay Gu about using GLC boosted tree to solve kaggle bike sharing challenge: |
| xgboost 0.4a21 | 6 | <img src=https://raw.githubusercontent.com/dmlc/dmlc.github.io/master/img/logo-m/xgboost.png width=135/> eXtreme Gradient Boosting =========== [](https://travis-ci.org/dmlc/xgboost) [](https://xgboost.readthedocs.org) [](http://cran.r-project.org/web/packages/xgboost) [](https://gitter.im/dmlc/xgboost?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge&utm_content=badge) An optimized general purpose gradient boosting library. The library is parallelized, and also provides an optimized distributed version. It implements machine learning algorithms under the [Gradient Boosting](https://en.wikipedia.org/wiki/Gradient_boosting) framework, including [Generalized Linear Model](https://en.wikipedia.org/wiki/Generalized_linear_model) (GLM) and [Gradient Boosted Decision Trees](https://en.wikipedia.org/wiki/Gradient_boosting#Gradient_tree_boosting) (GBDT). XGBoost can also be [distributed](#features) and scale to Terascale data XGBoost is part of [Distributed Machine Learning Common](http://dmlc.github.io/) <img src=https://avatars2.githubusercontent.com/u/11508361?v=3&s=20> projects Contents -------- * [What's New](#whats-new) * [Version](#version) * [Documentation](doc/index.md) * [Build Instruction](doc/build.md) * [Features](#features) * [Distributed XGBoost](multi-node) * [Usecases](doc/index.md#highlight-links) * [Bug Reporting](#bug-reporting) * [Contributing to XGBoost](#contributing-to-xgboost) * [Committers and Contributors](CONTRIBUTORS.md) * [License](#license) * [XGBoost in Graphlab Create](#xgboost-in-graphlab-create) What's New ---------- * XGBoost helps Owen Zhang to win the [Avito Context Ad Click competition](https://www.kaggle.com/c/avito-context-ad-clicks). Check out the [interview from Kaggle](http://blog.kaggle.com/2015/08/26/avito-winners-interview-1st-place-owen-zhang/). * XGBoost helps Chenglong Chen to win [Kaggle CrowdFlower Competition](https://www.kaggle.com/c/crowdflower-search-relevance) Check out the [winning solution](https://github.com/ChenglongChen/Kaggle_CrowdFlower) * XGBoost-0.4 release, see [CHANGES.md](CHANGES.md#xgboost-04) * XGBoost helps three champion teams to win [WWW2015 Microsoft Malware Classification Challenge (BIG 2015)](http://www.kaggle.com/c/malware-classification/forums/t/13490/say-no-to-overfitting-approaches-sharing) Check out the [winning solution](doc/README.md#highlight-links) * [External Memory Version](doc/external_memory.md) Version ------- * Current version xgboost-0.4 - [Change log](CHANGES.md) - This version is compatible with 0.3x versions Features -------- * Easily accessible through CLI, [python](https://github.com/dmlc/xgboost/blob/master/demo/guide-python/basic_walkthrough.py), [R](https://github.com/dmlc/xgboost/blob/master/R-package/demo/basic_walkthrough.R), [Julia](https://github.com/antinucleon/XGBoost.jl/blob/master/demo/basic_walkthrough.jl) * Its fast! Benchmark numbers comparing xgboost, H20, Spark, R - [benchm-ml numbers](https://github.com/szilard/benchm-ml) * Memory efficient - Handles sparse matrices, supports external memory * Accurate prediction, and used extensively by data scientists and kagglers - [highlight links](https://github.com/dmlc/xgboost/blob/master/doc/README.md#highlight-links) * Distributed version runs on Hadoop (YARN), MPI, SGE etc., scales to billions of examples. Bug Reporting ------------- * For reporting bugs please use the [xgboost/issues](https://github.com/dmlc/xgboost/issues) page. * For generic questions or to share your experience using xgboost please use the [XGBoost User Group](https://groups.google.com/forum/#!forum/xgboost-user/) Contributing to XGBoost ----------------------- XGBoost has been developed and used by a group of active community members. Everyone is more than welcome to contribute. It is a way to make the project better and more accessible to more users. * Check out [Feature Wish List](https://github.com/dmlc/xgboost/labels/Wish-List) to see what can be improved, or open an issue if you want something. * Contribute to the [documents and examples](https://github.com/dmlc/xgboost/blob/master/doc/) to share your experience with other users. * Please add your name to [CONTRIBUTORS.md](CONTRIBUTORS.md) after your patch has been merged. License ------- © Contributors, 2015. Licensed under an [Apache-2](https://github.com/dmlc/xgboost/blob/master/LICENSE) license. XGBoost in Graphlab Create -------------------------- * XGBoost is adopted as part of boosted tree toolkit in Graphlab Create (GLC). Graphlab Create is a powerful python toolkit that allows you to do data manipulation, graph processing, hyper-parameter search, and visualization of TeraBytes scale data in one framework. Try the [Graphlab Create](http://graphlab.com/products/create/quick-start-guide.html) * Nice [blogpost](http://blog.graphlab.com/using-gradient-boosted-trees-to-predict-bike-sharing-demand) by Jay Gu about using GLC boosted tree to solve kaggle bike sharing challenge: |
| xgboost 0.4a23 | 6 | <img src=https://raw.githubusercontent.com/dmlc/dmlc.github.io/master/img/logo-m/xgboost.png width=135/> eXtreme Gradient Boosting =========== [](https://travis-ci.org/dmlc/xgboost) [](https://xgboost.readthedocs.org) [](http://cran.r-project.org/web/packages/xgboost) [](https://gitter.im/dmlc/xgboost?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge&utm_content=badge) An optimized general purpose gradient boosting library. The library is parallelized, and also provides an optimized distributed version. It implements machine learning algorithms under the [Gradient Boosting](https://en.wikipedia.org/wiki/Gradient_boosting) framework, including [Generalized Linear Model](https://en.wikipedia.org/wiki/Generalized_linear_model) (GLM) and [Gradient Boosted Decision Trees](https://en.wikipedia.org/wiki/Gradient_boosting#Gradient_tree_boosting) (GBDT). XGBoost can also be [distributed](#features) and scale to Terascale data XGBoost is part of [Distributed Machine Learning Common](http://dmlc.github.io/) <img src=https://avatars2.githubusercontent.com/u/11508361?v=3&s=20> projects Contents -------- * [What's New](#whats-new) * [Version](#version) * [Documentation](doc/index.md) * [Build Instruction](doc/build.md) * [Features](#features) * [Distributed XGBoost](multi-node) * [Usecases](doc/index.md#highlight-links) * [Bug Reporting](#bug-reporting) * [Contributing to XGBoost](#contributing-to-xgboost) * [Committers and Contributors](CONTRIBUTORS.md) * [License](#license) * [XGBoost in Graphlab Create](#xgboost-in-graphlab-create) What's New ---------- * XGBoost helps Owen Zhang to win the [Avito Context Ad Click competition](https://www.kaggle.com/c/avito-context-ad-clicks). Check out the [interview from Kaggle](http://blog.kaggle.com/2015/08/26/avito-winners-interview-1st-place-owen-zhang/). * XGBoost helps Chenglong Chen to win [Kaggle CrowdFlower Competition](https://www.kaggle.com/c/crowdflower-search-relevance) Check out the [winning solution](https://github.com/ChenglongChen/Kaggle_CrowdFlower) * XGBoost-0.4 release, see [CHANGES.md](CHANGES.md#xgboost-04) * XGBoost helps three champion teams to win [WWW2015 Microsoft Malware Classification Challenge (BIG 2015)](http://www.kaggle.com/c/malware-classification/forums/t/13490/say-no-to-overfitting-approaches-sharing) Check out the [winning solution](doc/README.md#highlight-links) * [External Memory Version](doc/external_memory.md) Version ------- * Current version xgboost-0.4 - [Change log](CHANGES.md) - This version is compatible with 0.3x versions Features -------- * Easily accessible through CLI, [python](https://github.com/dmlc/xgboost/blob/master/demo/guide-python/basic_walkthrough.py), [R](https://github.com/dmlc/xgboost/blob/master/R-package/demo/basic_walkthrough.R), [Julia](https://github.com/antinucleon/XGBoost.jl/blob/master/demo/basic_walkthrough.jl) * Its fast! Benchmark numbers comparing xgboost, H20, Spark, R - [benchm-ml numbers](https://github.com/szilard/benchm-ml) * Memory efficient - Handles sparse matrices, supports external memory * Accurate prediction, and used extensively by data scientists and kagglers - [highlight links](https://github.com/dmlc/xgboost/blob/master/doc/README.md#highlight-links) * Distributed version runs on Hadoop (YARN), MPI, SGE etc., scales to billions of examples. Bug Reporting ------------- * For reporting bugs please use the [xgboost/issues](https://github.com/dmlc/xgboost/issues) page. * For generic questions or to share your experience using xgboost please use the [XGBoost User Group](https://groups.google.com/forum/#!forum/xgboost-user/) Contributing to XGBoost ----------------------- XGBoost has been developed and used by a group of active community members. Everyone is more than welcome to contribute. It is a way to make the project better and more accessible to more users. * Check out [Feature Wish List](https://github.com/dmlc/xgboost/labels/Wish-List) to see what can be improved, or open an issue if you want something. * Contribute to the [documents and examples](https://github.com/dmlc/xgboost/blob/master/doc/) to share your experience with other users. * Please add your name to [CONTRIBUTORS.md](CONTRIBUTORS.md) after your patch has been merged. License ------- © Contributors, 2015. Licensed under an [Apache-2](https://github.com/dmlc/xgboost/blob/master/LICENSE) license. XGBoost in Graphlab Create -------------------------- * XGBoost is adopted as part of boosted tree toolkit in Graphlab Create (GLC). Graphlab Create is a powerful python toolkit that allows you to do data manipulation, graph processing, hyper-parameter search, and visualization of TeraBytes scale data in one framework. Try the [Graphlab Create](http://graphlab.com/products/create/quick-start-guide.html) * Nice [blogpost](http://blog.graphlab.com/using-gradient-boosted-trees-to-predict-bike-sharing-demand) by Jay Gu about using GLC boosted tree to solve kaggle bike sharing challenge: |
| bayesredis 1.2.0 | 5 | A Simple Naive Bayes Classifier in Python |
| classipy 1.0.0 | 5 | a command-line based text classification tool |
| classipy 1.1.1 | 5 | a command-line based text classification tool |
| classipy 1.1.0 | 5 | a command-line based text classification tool |
| gradient-optimizers 0.0.4 | 5 | Python package for wrapping gradient optimizers for models in Theano |
| kohonen 1.1.2 | 5 | A library of vector quantizers |
| lmj.pursuit 0.3.1 | 5 | A library of matching pursuit implementations |
| MDP 3.3 | 5 | MDP is a Python library for building complex data processing software by combining widely used machine learning algorithms into pipelines and networks. |
| mltool 0.6 | 5 | Machine learning tool for regression. |
| neurolab 0.3.5 | 5 | Simple and powerfull neural network library for python |
| nn 2.0.0 | 5 | Multilayer perceptron (MLP) network implementation in Python |
| pyaixi 1.0.4 | 5 | A pure Python implementation of the Monte Carlo-AIXI-Context Tree Weighting (MC-AIXI-CTW) artificial intelligence algorithm. |
| pydnn 0.0.dev | 5 | deep neural network library in Python |
| pyfora 0.1 | 5 | A library for parallel execution of Python code in the Ufora runtime |
| pyfora 0.1a3 | 5 | A library for parallel execution of Python code in the Ufora runtime |
| pyfora 0.2rc1 | 5 | A library for parallel execution of Python code in the Ufora runtime |
| pyfora 0.2.1 | 5 | A library for parallel execution of Python code in the Ufora runtime |
| pyfora 0.2rc2 | 5 | A library for parallel execution of Python code in the Ufora runtime |
| pyfora 0.2 | 5 | A library for parallel execution of Python code in the Ufora runtime |
| python-weka-wrapper 0.3.2 | 5 | Python wrapper for the Weka Machine Learning Workbench |
| python-weka-wrapper 0.3.3 | 5 | Python wrapper for the Weka Machine Learning Workbench |
| RLToolkit 1.0 | 5 | |
| aima 2015.4.5 | 4 | aima -- Artificial Intelligence, A Modern Approach, by Stuart Russell and Peter Norvig |
| apgl 0.8.1 | 4 | A fast python graph library based on numpy and scipy. |
| arachnid 0.1.7 | 4 | Single Particle Data Analysis Suite |
| autograd 1.1.3 | 4 | Efficiently computes derivatives of numpy code. |
| autograd 1.0.9 | 4 | Efficiently computes derivatives of numpy code. |
| autograd 1.1.0 | 4 | Efficiently computes derivatives of numpy code. |
| autograd 1.0.4 | 4 | Efficiently computes derivatives of numpy code. |
| autograd 1.1.1 | 4 | Efficiently computes derivatives of numpy code. |
| autograd 1.1.2 | 4 | Efficiently computes derivatives of numpy code. |
| autograd 1.0.5 | 4 | Efficiently computes derivatives of numpy code. |
| autograd 1.0.6 | 4 | Efficiently computes derivatives of numpy code. |
| Azimuth 0.2 | 4 | Machine Learning-Based Predictive Modelling of CRISPR/Cas9 guide efficiency |
| azureml 0.2.2 | 4 | Microsoft Azure Machine Learning Python client library |
| azureml 0.2.3 | 4 | Microsoft Azure Machine Learning Python client library |
| azureml 0.2.4 | 4 | Microsoft Azure Machine Learning Python client library |
| azureml 0.2.5 | 4 | Microsoft Azure Machine Learning Python client library |
| azureml 0.2.6 | 4 | Microsoft Azure Machine Learning Python client library |
| bat-country 0.2 | 4 | A lightweight, extendible, easy to use Python package for deep dreaming and image generation with Caffe and CNNs |
| bllipparser 2015.12.3 | 4 | Python bindings for the BLLIP natural language parser |
| bllipparser 2015.08.18 | 4 | Python bindings for the BLLIP natural language parser |
| bob.learn.em 2.0.5 | 4 | Bindings for emelaneous machines and trainers of Bob |
| bob.learn.em 2.0.6 | 4 | Bindings for emelaneous machines and trainers of Bob |
| bob.learn.em 2.0.7 | 4 | Bindings for emelaneous machines and trainers of Bob |
| buluml 0.0.1 | 4 | bulu Dog's machine learning library |
| cloudbiolinux 0.3a | 4 | configure virtual (or real) machines with tools for biological analyses |
| Cluster_Ensembles 1.0 | 4 | A package for determining the consensus clustering from an ensemble of partitions |
| Cluster_Ensembles 1.6 | 4 | A package for determining the consensus clustering from an ensemble of partitions |
| Cluster_Ensembles 1.14 | 4 | A package for determining the consensus clustering from an ensemble of partitions |
| Cluster_Ensembles 1.9 | 4 | A package for determining the consensus clustering from an ensemble of partitions |
| cox-nnet 0.1 | 4 | Extension of neural network architecture for Cox Regression |
| cox-nnet 0.202 | 4 | Extension of neural networks for Cox Regression |
| cudnn-python-wrappers 2.0b2 | 4 | Python wrappers for the NVIDIA cudnn 6.5 R2 libraries. |
| decision_tree 0.04 | 4 | Practice implementation of a classification decision tree |
| decision_tree 0.03 | 4 | Practice implementation of a classification decision tree |
| decision_tree 0.01 | 4 | Practice implementation of a classification decision tree |
| decision_tree 0.02 | 4 | Practice implementation of a classification decision tree |
| destimator 0.0.4 | 4 | A metadata-saving proxy for scikit-learn etimators. |
| destimator 0.0.2 | 4 | A metadata-saving proxy for scikit-learn etimators. |
| destimator 0.0.5 | 4 | A metadata-saving proxy for scikit-learn etimators. |
| discomll 0.1.3 | 4 | Disco Machine Learning Library. |
| discomll 0.1.4 | 4 | Disco Machine Learning Library. |
| discomll 0.1.4.1 | 4 | Disco Machine Learning Library. |
| discomll 0.1.4.2 | 4 | Disco Machine Learning Library. |
| dreaml 0.0.2 | 4 | Dynamic Reactive Machine learning |
| easymlserver 0.1.2 | 4 | Server package for EasyML-lib (Android machine learning) |
| estnltk-textclassifier 1.2.2 | 4 | Machine learning software for organizing data into categories |
| explain_sklearn 0.0.2 | 4 | Turn any scikit-learn classifier into an interpretable model by using a lightweight wrapper. |
| featureimpact 1.1.0 | 4 | Compute the statistical impact of features given a scikit-learn estimator |
| ffx 1.3.4 | 4 | Fast Function Extraction: A fast, scalable, and deterministic symbolic regression tool. |
| genalg 1.0.3 | 4 | A generalizable genetic algorithm package written in Python. |
| genalg 1.0.2 | 4 | A generalizable genetic algorithm package written in Python. |
| genalg 1.0.1 | 4 | A generalizable genetic algorithm package written in Python. |
| glove 1.0.0 | 4 | Python package for computing embeddings from co-occurence matrices |
| glowfi.sh 0.3.51 | 4 | Machine learning without the PhD. Now with machine guns and rocket launchers. |
| GraphLab-Create 1.5.2 | 4 | GraphLab Create enables developers and data scientists to apply machine learning to build state of the art data products. |
| GraphLab-Create 1.6 | 4 | GraphLab Create enables developers and data scientists to apply machine learning to build state of the art data products. |
| GraphLab-Create 1.6.1 | 4 | GraphLab Create enables developers and data scientists to apply machine learning to build state of the art data products. |
| GraphLab-Create 1.7.1 | 4 | GraphLab Create enables developers and data scientists to apply machine learning to build state of the art data products. |
| hessianfree 0.1 | 4 | Hessian-free optimization for deep networks |
| hessianfree 0.1.2 | 4 | Hessian-free optimization for deep networks |
| hessianfree 0.1.4 | 4 | Hessian-free optimization for deep networks |
| hessianfree 0.1.5 | 4 | Hessian-free optimization for deep networks |
| hessianfree 0.1.6 | 4 | Hessian-free optimization for deep networks |
| hessianfree 0.2.1 | 4 | Hessian-free optimization for deep networks |
| hessianfree 0.3.0 | 4 | Hessian-free optimization for deep networks |
| hessianfree 0.3.1 | 4 | Hessian-free optimization for deep networks |
| hessianfree 0.3.3 | 4 | Hessian-free optimization for deep networks |
| hessianfree 0.3.4 | 4 | Hessian-free optimization for deep networks |
| hydrat 0.9.5 | 4 | Classifier comparison framework |
| hyperspy 0.8.2 | 4 | Multidimensional data analysis toolbox |
| IndicoIo 0.9.2 | 4 | A Python Wrapper for indico. Use pre-built state of the art machine learning algorithms with a single line of code. |
| IndicoIo 0.9.3 | 4 | A Python Wrapper for indico. Use pre-built state of the art machine learning algorithms with a single line of code. |
| IndicoIo 0.10.0 | 4 | A Python Wrapper for indico. Use pre-built state of the art machine learning algorithms with a single line of code. |
| IndicoIo 0.10.1 | 4 | A Python Wrapper for indico. Use pre-built state of the art machine learning algorithms with a single line of code. |
| IndicoIo 0.10.2 | 4 | A Python Wrapper for indico. Use pre-built state of the art machine learning algorithms with a single line of code. |
| IndicoIo 0.10.3 | 4 | A Python Wrapper for indico. Use pre-built state of the art machine learning algorithms with a single line of code. |
| IndicoIo 0.11.0 | 4 | A Python Wrapper for indico. Use pre-built state of the art machine learning algorithms with a single line of code. |
| IndicoIo 0.11.1 | 4 | A Python Wrapper for indico. Use pre-built state of the art machine learning algorithms with a single line of code. |
| IndicoIo 0.11.2 | 4 | A Python Wrapper for indico. Use pre-built state of the art machine learning algorithms with a single line of code. |
| ionmf 1.1 | 4 | Integrative orthogonal non-negative matrix factorization with examples. |
| ionmf 0.3.5 | 4 | Integrative orthogonal non-negative matrix factorization with examples. |
| jubatus 0.8.2 | 4 | Jubatus is a distributed processing framework and streaming machine learning library. This is the Jubatus client in Python. |
| kHLL 0.0.4 | 4 | Memory saving and fast k-deterministic k-means with HyperLogLog |
| krakenous 0.3 | 4 | A backend for machine learning-related feature extraction and storing |
| libextract 0.0.12 | 4 | A HT/XML web scraping tool |
| lmj.particle 0.1.1 | 4 | A library of generic particle filters |
| lmj.perceptron 0.1.1 | 4 | A library of perceptrons |
| lmj.tagger 0.1.1 | 4 | A tagger for sequence data |
| mang 0.1.3.2 | 4 | Another neural network library for python based on cudamat |
| MarkovEquClasses 1.0.1 | 4 | Algorithms for exploring Markov equivalence classes: MCMC, size counting |
| minitrace 0.2 | 4 | A module for machine learning models with trace norm penalties |
| ml_metrics 0.1.4 | 4 | Machine Learning Evaluation Metrics |
| mlboost 0.4.1 | 4 | an innovative machine learning library for extreme prototyping |
| mlxtend 0.2.8 | 4 | Machine Learning Library Extensions |
| mlxtend 0.2.9 | 4 | Machine Learning Library Extensions |
| monkeylearn 0.1.1 | 4 | UNKNOWN |
| monkeylearn 0.2.1 | 4 | Official Python client for the MonkeyLearn API |
| monkeylearn 0.2 | 4 | Official Python client for the MonkeyLearn API |
| NaiveBayes 1.0.0 | 4 | A Naive Bayes classifier |
| neokami-sdk 0.2 | 4 | Python sdk for Neokami API |
| neokami-sdk 0.1.1 | 4 | Python sdk for Neokami API |
| nervananeon 0.8.1 | 4 | Deep learning framework with configurable backends |
| nlup 0.5 | 4 | ('Core libraries for natural language processing',) |
| nnet-ts 0.6 | 4 | Neural network architecture for time series forecasting. |
| nonconformist 1.2.5 | 4 | Python implementation of the conformal prediction framework. |
| openfst 1.5.0 | 4 | Python wrapper for OpenFst |
| Orange 2.7.8 | 4 | Orange, a component-based data mining framework. |
| Orange-Bioinformatics 2.6.13 | 4 | Orange Bioinformatics add-on for Orange data mining software package. |
| Orange-Bioinformatics 2.6.12 | 4 | Orange Bioinformatics add-on for Orange data mining software package. |
| Orange-Bioinformatics 2.6.14 | 4 | Orange Bioinformatics add-on for Orange data mining software package. |
| Orange-ModelMaps 0.2.8 | 4 | Orange Model Maps (space of prediction models) add-on for Orange data mining software package. |
| Orange-Multitarget 0.9.3 | 4 | Orange Multitarget add-on for Orange data mining software package. |
| Orange-Network 0.3.4 | 4 | Orange Network add-on for Orange data mining software package. |
| Orange-NMF 0.1.2 | 4 | Orange NMF add-on for Orange data mining software package. |
| Orange-Reliability 0.2.14 | 4 | Orange Reliability add-on for Orange data mining software package. |
| Orange-Text 1.2a1 | 4 | Orange Text Mining add-on for Orange data mining software package. |
| Orange3-Network 1.1.0 | 4 | Networks add-on for Orange 3 data mining software package. |
| Orange3-Network 1.0.4 | 4 | Networks add-on for Orange 3 data mining software package. |
| Orange3-spark 0.2.2 | 6 | A series of Widgets for Orange3 to work with Spark ML |
| Orange3-spark 0.2.0 | 6 | A series of Widgets for Orange3 to work with Spark ML |
| Orange3-spark 0.2.1 | 6 | A series of Widgets for Orange3 to work with Spark ML |
| othello 1.0b1 | 4 | Implementation of Othello/Reversi for AI course instruction |
| pandas_confusion 0.0.4 | 4 | Pandas matrix confusion with plot features (matplotlib, seaborn...) |
| pandas_confusion 0.0.6 | 4 | Pandas matrix confusion with plot features (matplotlib, seaborn...) |
| paramz 0.0.33 | 4 | The Parameterization Framework |
| paramz 0.0.7 | 4 | The Parameterization Framework |
| paramz 0.0.6 | 4 | The Parameterization Framework |
| paramz 0.0.22 | 4 | The Parameterization Framework |
| paramz 0.0.23 | 4 | The Parameterization Framework |
| paramz 0.0.32 | 4 | The Parameterization Framework |
| paramz 0.1.0 | 4 | The Parameterization Framework |
| paramz 0.0.30 | 4 | The Parameterization Framework |
| paramz 0.0.19 | 4 | The Parameterization Framework |
| paramz 0.0.35 | 4 | The Parameterization Framework |
| paramz 0.0.34 | 4 | The Parameterization Framework |
| paramz 0.0.2 | 4 | The Parameterization Framework |
| paramz 0.0.8 | 4 | The Parameterization Framework |
| paramz 0.0.12 | 4 | The Parameterization Framework |
| paramz 0.0.11 | 4 | The Parameterization Framework |
| paramz 0.1.1 | 4 | The Parameterization Framework |
| paramz 0.0.10 | 4 | The Parameterization Framework |
| practnlptools 1.0 | 4 | Practical Natural Language Processing Tools for Humans. Dependency Parsing, Syntactic Constituent Parsing, Semantic Role Labeling, Named Entity Recognisation, Shallow chunking, Part of Speech Tagging, all in Python. |
| prox_tv 3.1.1 | 4 | Toolbox for fast Total Variation proximity operators |
| pug 0.1.22 | 4 | Meta package to install the PDX Python User Group utilities. |
| pug-ann 0.0.22 | 4 | # pug-ann |
| pug-nlp 0.0.21 | 4 | # pug-nlp |
| py-enigma 0.1 | 4 | A historically accurate Enigma machine simulation library. |
| py-sam 0.1 | 4 | the spherical admixture topic model |
| PyBrain 0.3.3 | 4 | PyBrain is the Swiss army knife for neural networking. |
| pygfl 1.0.1 | 4 | A Fast and Flexible Graph-Fused Lasso Solver |
| pygfl 1.0.0 | 4 | A Fast and Flexible Graph-Fused Lasso Solver |
| PyMissingData 1.1.2 | 4 | An approach based on Bayesian Networks to fill missing values |
| pyoxford 0.3.0 | 4 | Python library to access Microsoft Project Oxford |
| pyoxford 0.2.0 | 4 | Python library to access Microsoft Project Oxford |
| pyoxford 0.1.1 | 4 | Python library to access Microsoft Project Oxford |
| PyStanfordDependencies 0.3.0 | 4 | Python interface for converting Penn Treebank trees to Stanford Dependencies and Universal Dependencies |
| PyStanfordDependencies 0.3.1 | 4 | Python interface for converting Penn Treebank trees to Universal Dependencies and Stanford Dependencies |
| PyTLDR 0.1.4 | 4 | A module to perform automatic article summarization. |
| pytreebank 0.1.4 | 4 | Python package for loading Stanford Sentiment Treebank corpus |
| pytreebank 0.1.3 | 4 | Python package for loading Stanford Sentiment Treebank corpus |
| pytreebank 0.1.7 | 4 | Python package for loading Stanford Sentiment Treebank corpus |
| pywFM 0.5 | 4 | Python wrapper for libFM |
| pyxval 0.9.3 | 4 | Machine learning cross-validation framework |
| rep 0.6.4 | 4 | infrastructure for computational experiments on shared big datasets |
| rep 0.6.3 | 4 | infrastructure for computational experiments on shared big datasets |
| Savitzky-Golay-Filters 1.0 | 4 | Savitzky Golay Filters for smoothing functions |
| SFrame 0.1 | 4 | SFrame enables developers and data scientists to apply machine learning to build state of the art data products. |
| sklearn 0.0 | 4 | A set of python modules for machine learning and data mining |
| smoothfdr 0.9.4 | 4 | False discovery rate smoothing |
| smoothfdr 0.9.1 | 4 | False discovery rate smoothing |
| spark-ml-streaming 0.1.0 | 4 | A Python library for visualizing streaming machine learning in Spark |
| stolos 2.0.1 | 4 | A DAG-based job queueing system and executor for performing work with complex dependency requirements between applications |
| stolos 2.0.0 | 4 | A DAG-based job queueing system and executor for performing work with complex dependency requirements between applications |
| stolos 1.1.0 | 4 | A DAG-based job queueing system and executor for performing work with complex dependency requirements between applications |
| tableh 0.0.01 | 4 | Tableh, taking the "Matt Damon - Oscar Winning actor" out of "Mahhttt Dahhmonnn. |
| test_helper 0.2 | 4 | A testing helper for scalable machine learning mooc |
| vowpal_porpoise 0.3 | 4 | Lightweight vowpal wabbit wrapper |
| wordgrapher 0.3.1 | 4 | Word Graph utility built with NLTK and TextBlob |
| xtoy 0.0.1 | 4 | get xtoyed predictions from raw data |
| xtoy 0.0.24 | 4 | get xtoyed predictions from raw data |
| yard 0.2.3 | 4 | Yet another ROC curve drawer |
| zChainer 0.1.3 | 4 | scikit-learn like interface and stacked autoencoder for chainer |
| zChainer 0.1.4 | 4 | scikit-learn like interface and stacked autoencoder for chainer |
| zChainer 0.2.1 | 4 | scikit-learn like interface and stacked autoencoder for chainer |
| ztilde 0.4 | 4 | Python client lib for ztilde.com machine learning services |
| bob.learn.activation 2.0.3 | 3 | Bindings for bob.machine's Activation functors |
| bob.learn.activation 2.0.4 | 3 | Bindings for bob.machine's Activation functors |
| ad3 2.0.2 | 2 | UNKNOWN |
| agd_tools 0.0.1 | 2 | UNKNOWN |
| alchemyapi_python 1.2.1 | 2 | Enhanced version of AlchemyAPI Python SDK |
| antispoofing.clientspec 1.0.1 | 2 | Building client-specific models for anti-spoofing |
| antispoofing.competition_icb2013 1.1.0 | 2 | Fusion of spoofing counter measures for the REPLAY-ATTACK database (competition entry for 2nd competition on counter measures to 2D facial spoofing attacks, ICB 2013) |
| antispoofing.crossdatabase 1.0.1 | 2 | Antispoofing cross database testing |
| antispoofing.dog 1.0.2 | 2 | Idiap's implementation for the paper: A face Antispoofing Database with Diverse Attacks |
| antispoofing.evaluation 2.0.2 | 2 | Evaluation tools for verification systems under spoofing attacks: examples in face verification |
| antispoofing.evaluation 2.0.4 | 2 | Evaluation tools for verification systems under spoofing attacks: examples in face verification |
| antispoofing.eyeblink 1.0.4 | 2 | Eye-blinking counter-measures for the REPLAY-ATTACK database |
| antispoofing.fusion 2.0.1 | 2 | Complementary countermeasures for detecting scenic face spoofing attacks |
| antispoofing.fusion_faceverif 3.0.0 | 2 | Decision and score-level fusion tools for joint operation of face verification and anti-spoofing system |
| antispoofing.fusion_faceverif 3.0.1 | 2 | Decision and score-level fusion tools for joint operation of face verification and anti-spoofing system |
| antispoofing.fusion_faceverif 3.0.2b0 | 2 | Decision and score-level fusion tools for joint operation of face verification and anti-spoofing system |
| antispoofing.lbp 2.0.2 | 2 | Texture (LBP) based counter-measures for the REPLAY-ATTACK database |
| antispoofing.lbptop 2.0.0 | 2 | LBP-TOP based countermeasure against facial spoofing attacks |
| antispoofing.lbptop 2.0.2 | 2 | LBP-TOP based countermeasure against facial spoofing attacks |
| antispoofing.motion 2.0.1 | 2 | Motion counter-measures for the REPLAY-ATTACK database |
| antispoofing.optflow 2.0.0 | 2 | Optical Flow counter-measures for the REPLAY-ATTACK database |
| antispoofing.utils 2.0.6 | 2 | Utility package for anti-spoofing countermeasures |
| antispoofing.utils 2.0.7 | 2 | Utility package for anti-spoofing countermeasures |
| antispoofing.verification.gmm 1.0.2 | 2 | Replay-Attack Face Verification Package Based on a Parts-Based Gaussian Mixture Models |
| astroML_addons 0.2.2 | 2 | Performance add-ons for the astroML package |
| bayespy 0.3.6 | 2 | Variational Bayesian inference tools for Python |
| bayespy 0.3.7 | 2 | Variational Bayesian inference tools for Python |
| bayespy 0.4.0 | 2 | Variational Bayesian inference tools for Python |
| bayespy 0.4.1 | 2 | Variational Bayesian inference tools for Python |
| bdot 0.1.6 | 2 | Fast Dot Products on Pretty Big Data (powered by Bcolz) |
| bdot 0.1.7 | 2 | Fast Dot Products on Pretty Big Data (powered by Bcolz) |
| bebop.protocol 0.1 | 2 | This package allows to register components from Python. It also provides a basic implementation of generic functions in Zope3 |
| bigml 4.1.7 | 2 | An open source binding to BigML.io, the public BigML API |
| bigml 4.2.0 | 2 | An open source binding to BigML.io, the public BigML API |
| bigml 4.2.1 | 2 | An open source binding to BigML.io, the public BigML API |
| bigml 4.2.2 | 2 | An open source binding to BigML.io, the public BigML API |
| bigml 4.3.0 | 2 | An open source binding to BigML.io, the public BigML API |
| bigml 4.3.1 | 2 | An open source binding to BigML.io, the public BigML API |
| bigml 4.3.2 | 2 | An open source binding to BigML.io, the public BigML API |
| bigml 4.3.3 | 2 | An open source binding to BigML.io, the public BigML API |
| bigml 4.3.4 | 2 | An open source binding to BigML.io, the public BigML API |
| bigml 4.4.0 | 2 | An open source binding to BigML.io, the public BigML API |
| bigml 4.4.1 | 2 | An open source binding to BigML.io, the public BigML API |
| bigmler 3.2.1 | 2 | A command-line tool for BigML.io, the public BigML API |
| bigmler 3.3.0 | 2 | A command-line tool for BigML.io, the public BigML API |
| bigmler 3.3.1 | 2 | A command-line tool for BigML.io, the public BigML API |
| bigmler 3.3.2 | 2 | A command-line tool for BigML.io, the public BigML API |
| bigmler 3.3.3 | 2 | A command-line tool for BigML.io, the public BigML API |
| bigmler 3.3.4 | 2 | A command-line tool for BigML.io, the public BigML API |
| bigmler 3.3.5 | 2 | A command-line tool for BigML.io, the public BigML API |
| bigmler 3.3.6 | 2 | A command-line tool for BigML.io, the public BigML API |
| bigmler 3.3.7 | 2 | A command-line tool for BigML.io, the public BigML API |
| bigmler 3.3.8 | 2 | A command-line tool for BigML.io, the public BigML API |
| bob.bio.csu 2.0.1 | 2 | Wrapper classes to use the PythonFaceEvaluation classes from the CSU Face Recognition Resources |
| bob.bio.csu 2.0.2 | 2 | Wrapper classes to use the PythonFaceEvaluation classes from the CSU Face Recognition Resources |
| bob.buildout 2.0.6 | 2 | zc.buildout recipes to perform a variety of tasks required by Bob satellite packages |
| bob.buildout 2.0.7 | 2 | zc.buildout recipes to perform a variety of tasks required by Bob satellite packages |
| bob.buildout 2.0.8 | 2 | zc.buildout recipes to perform a variety of tasks required by Bob satellite packages |
| bob.buildout 2.0.9 | 2 | zc.buildout recipes to perform a variety of tasks required by Bob satellite packages |
| bob.extension 2.0.8 | 2 | Building of Python/C++ extensions for Bob |
| bob.extension 2.0.10 | 2 | Building of Python/C++ extensions for Bob |
| bob.ip.flandmark 2.0.2 | 2 | Python bindings to the flandmark keypoint localization library |
| bob.ip.flandmark 2.0.3 | 2 | Python bindings to the flandmark keypoint localization library |
| bob.ip.optflow.hornschunck 2.0.5 | 2 | Python bindings to the optical flow framework of Horn & Schunck |
| bob.ip.optflow.hornschunck 2.0.6 | 2 | Python bindings to the optical flow framework of Horn & Schunck |
| bob.ip.optflow.liu 2.0.4 | 2 | Python bindings to the optical flow framework by C. Liu |
| bob.ip.optflow.liu 2.0.5 | 2 | Python bindings to the optical flow framework by C. Liu |
| bob.palmvein 2.0.0a1 | 2 | Palmvein recognition based on Bob and the facereclib |
| bob.paper.ICB2015 2.0.0a0 | 2 | Running the experiments as given in paper: "On the Vulnerability of Palm Vein Recognition to Spoofing Attacks". |
| bob.thesis.ichingo2015 0.0.0 | 2 | Trustworthy biometric recognition under spoofing attacks: application to the face mode |
| bob.thesis.ichingo2015 0.0.1 | 2 | Trustworthy biometric recognition under spoofing attacks: application to the face mode |
| boto 2.38.0 | 2 | Amazon Web Services Library |
| boto-patch 2.38.0 | 2 | Amazon Web Services Library |
| bt 0.1.10 | 2 | A flexible backtesting framework for Python |
| bt 0.1.12 | 2 | A flexible backtesting framework for Python |
| bt 0.1.13 | 2 | A flexible backtesting framework for Python |
| caerbannog 0.1 | 2 | Well, that's no ordinary rabbit. |
| canari 1.1 | 2 | Rapid transform development and transform execution framework for Maltego. |
| ccsnmultivar 0.0.5 | 2 | Multivariate regression analysis of core-collapse simulations |
| cec2013lsgo 0.1 | 2 | Package for benchmark for the Real Large Scale Global Optimization session on IEEE Congress on Evolutionary Computation CEC'2013 |
| ChatterBot 0.2.4 | 2 | An open-source chat bot program written in Python. |
| ChatterBot 0.2.5 | 2 | An open-source chat bot program written in Python. |
| ChatterBot 0.2.6 | 2 | An open-source chat bot program written in Python. |
| ChatterBot 0.2.7 | 2 | An open-source chat bot program written in Python. |
| ChatterBot 0.2.8 | 2 | An open-source chat bot program written in Python. |
| ChatterBot 0.2.9 | 2 | An open-source chat bot program written in Python. |
| ChatterBot 0.3.0 | 2 | An open-source chat bot program written in Python. |
| ChatterBot 0.3.1 | 2 | An open-source chat bot program written in Python. |
| ChatterBot 0.3.2 | 2 | An open-source chat bot program written in Python. |
| ck 1.5.0915 | 2 | Collective Knowledge - lightweight knowledge manager to organize, cross-link, share and reuse artifacts |
| ck 1.5.0916 | 2 | Collective Knowledge - lightweight knowledge manager to organize, cross-link, share and reuse artifacts |
| ck 1.5.0917 | 2 | Collective Knowledge - lightweight knowledge manager to organize, cross-link, share and reuse artifacts |
| ck 1.6.2 | 2 | Collective Knowledge - lightweight knowledge manager to organize, cross-link, share and reuse artifacts |
| ck 1.6.4 | 2 | Collective Knowledge - lightweight knowledge manager to organize, cross-link, share and reuse artifacts |
| ck 1.6.5 | 2 | Collective Knowledge - lightweight knowledge manager to organize, cross-link, share and reuse artifacts |
| ck 1.6.6 | 2 | Collective Knowledge - lightweight knowledge manager to organize, cross-link, share and reuse artifacts |
| ck 1.6.8 | 2 | Collective Knowledge - lightweight knowledge manager to organize, cross-link, share and reuse artifacts |
| ck 1.6.9 | 2 | Collective Knowledge - lightweight knowledge manager to organize, cross-link, share and reuse artifacts |
| ck 1.6.11 | 2 | Collective Knowledge - lightweight knowledge manager to organize, cross-link, share and reuse artifacts |
| ck 1.6.12 | 2 | Collective Knowledge - lightweight knowledge manager to organize, cross-link, share and reuse artifacts |
| claw 1.2.0 | 2 | Library to extract message quotations and signatures. |
| claw 1.3.0 | 2 | Library to extract message quotations and signatures. |
| codalab-cli 0.1.9 | 2 | Codalab CLI is a command-line tool for interacting with Codalab. See http://codalab.org/ |
| constractor 0.1.0 | 2 | Smart web page content extractor. |
| cosmoabc 1.0.5 | 2 | Python ABC sampler |
| cotede 0.14.1 | 2 | Quality Control of Temperature and Salinity profiles |
| cotede 0.14.2 | 2 | Quality Control of Temperature and Salinity profiles |
| coursera 0.1.0a3 | 2 | Script for downloading Coursera.org videos and naming them. |
| cubicweb-semnews 0.2.0 | 2 | Cube for news storage and analysis |
| datagami-python 0.0.5 | 2 | Datagami library for Python |
| deap 1.0.2 | 2 | Distributed Evolutionary Algorithms in Python |
| dedupe 1.1.0 | 2 | A python library for accurate and scaleable data deduplication and entity-resolution |
| dedupe 1.1.1 | 2 | A python library for accurate and scaleable data deduplication and entity-resolution |
| dedupe 1.1.2 | 2 | A python library for accurate and scaleable data deduplication and entity-resolution |
| dedupe 1.1.4 | 2 | A python library for accurate and scaleable data deduplication and entity-resolution |
| dedupe 1.2.0 | 2 | A python library for accurate and scaleable data deduplication and entity-resolution |
| dedupe 1.2.1 | 2 | A python library for accurate and scaleable data deduplication and entity-resolution |
| dedupe 1.2.2 | 2 | A python library for accurate and scaleable data deduplication and entity-resolution |
| Density_Sampling 1.0 | 2 | For a dataset comprising a mixture of rare and common populations, density sampling gives equal weights to the representatives of those distinct populations. |
| Density_Sampling 1.1 | 2 | For a dataset comprising a mixture of rare and common populations, density sampling gives equal weights to the representatives of those distinct populations. |
| dicttokv 0.1.5 | 2 | `dicttokv` converts nested dictionary and list object into key-value tuples. |
| distributions 2.1.0 | 2 | Primitives for Bayesian MCMC inference |
| django-analyze 0.4.23 | 2 | A general purpose framework for training and testing classification algorithms. |
| dragnet 1.0.1 | 2 | Extract the main article content (and optionally comments) from a web page |
| dukedeploy 0.1.5 | 2 | Predictive model deployment with Duke Analytics. |
| epitopes 0.3.2 | 2 | Python interface to IEDB and other immune epitope data |
| estimate.gender 0.4 | 2 | Gender estimation on several databases |
| evogrid 0.1.0 | 2 | Distributed Evolutionary Computation framework |
| experimentator 0.3.0 | 2 | Experiment builder |
| facereclib 2.1.1 | 2 | Compare a variety of face recognition algorithms by running them on many image databases with default protocols. |
| FATS 1.3.6 | 2 | Library with compilation of features for time series |
| fms 0.1.9 | 2 | A Financial Market Simulator |
| FoLiA-tools 0.12.1.39 | 2 | FoLiA-tools contains various Python-based command line tools for working with FoLiA XML (Format for Linguistic Annotation) |
| FoLiA-tools 0.12.1.40 | 2 | FoLiA-tools contains various Python-based command line tools for working with FoLiA XML (Format for Linguistic Annotation) |
| FoLiA-tools 0.12.1.43 | 2 | FoLiA-tools contains various Python-based command line tools for working with FoLiA XML (Format for Linguistic Annotation) |
| FoLiA-tools 0.12.2.44 | 2 | FoLiA-tools contains various Python-based command line tools for working with FoLiA XML (Format for Linguistic Annotation) |
| FoLiA-tools 0.12.2.45 | 2 | FoLiA-tools contains various Python-based command line tools for working with FoLiA XML (Format for Linguistic Annotation) |
| formasaurus 0.2 | 2 | HTML form type detector |
| gplearn 0.1.0 | 2 | Genetic Programming in Python, with a scikit-learn inspired API |
| GPy 0.8.8 | 2 | The Gaussian Process Toolbox |
| gsh 0.12.3 | 2 | Pluggable Distributed SSH Command Executer. |
| halcon 0.0.1 | 2 | Python implementation of FALCON: Feedback Adaptive Loop for Content-Based Retrieval |
| hashedindex 0.4.0 | 2 | InvertedIndex implementation using hash lists (dictionaries) |
| hcpre 0.5.5 | 2 | Generalized launcher for human connectome project BOLD preprocessing |
| hdidx 0.2.2.3 | 2 | ANN Search in High-Dimensional Spaces |
| hdidx 0.2.3 | 2 | ANN Search in High-Dimensional Spaces |
| hdidx 0.2.8 | 2 | ANN Search in High-Dimensional Spaces |
| hwit-examples 0.01-r00018 | 2 | Examples for use with HWIT |
| ibmdbpy 0.1.0b2 | 6 | A Pandas-like SQL-wrapper for in-database analytics with IBM dashDB/DB2. |
| infpy 0.4.13 | 2 | A python inference library |
| intuition 0.4.3 | 2 | A trading system building blocks |
| invenio-classifier 0.1.0 | 2 | Invenio module for record classification. |
| irlib 0.1.1 | 2 | Inforamtion Retrieval Library |
| Kaggler 0.4.0 | 2 | Code for Kaggle Data Science Competitions. |
| Kotti 1.1.5 | 2 | A high-level, Pythonic web application framework based on Pyramid and SQLAlchemy. It includes an extensible Content Management System called the Kotti CMS. |
| Kotti 1.2.0 | 2 | A high-level, Pythonic web application framework based on Pyramid and SQLAlchemy. It includes an extensible Content Management System called the Kotti CMS. |
| Kotti 1.2.1 | 2 | A high-level, Pythonic web application framework based on Pyramid and SQLAlchemy. It includes an extensible Content Management System called the Kotti CMS. |
| Kotti 1.2.2 | 2 | A high-level, Pythonic web application framework based on Pyramid and SQLAlchemy. It includes an extensible Content Management System called the Kotti CMS. |
| Kotti 1.2.4 | 2 | A high-level, Pythonic web application framework based on Pyramid and SQLAlchemy. It includes an extensible Content Management System called the Kotti CMS. |
| liac-arff 2.1.0 | 2 | A module for read and write ARFF files in Python. |
| Lifetimes 0.1.6.2 | 2 | Measure customer lifetime value in Python |
| Lifetimes 0.1.6.3 | 2 | Measure customer lifetime value in Python |
| luigi 1.3.0 | 2 | Workflow mgmgt + task scheduling + dependency resolution |
| luigi 2.0.0 | 2 | Workflow mgmgt + task scheduling + dependency resolution |
| luigi 2.0.1 | 2 | Workflow mgmgt + task scheduling + dependency resolution |
| madmom 0.12 | 2 | Python audio signal processing library |
| maskattack.lbp 1.0.4 | 2 | Texture (LBP) based counter-measures for the 3D MASK ATTACK database |
| maskattack.study 1.0.0 | 2 | Accumulate depth frames of 3DMAD database for better face models and analyze verification and spoofing performances of 2D, 2.5D and 3D samples |
| MD-ELM 0.61 | 2 | Mislabeled samples detection with OP-ELM |
| metaheuristic-algorithms-python 0.1.0 | 2 | Various metaheuristic algorithms implemented in Python |
| metaheuristic-algorithms-python 0.1.1 | 2 | Various metaheuristic algorithms implemented in Python |
| metaheuristic-algorithms-python 0.1.2 | 2 | Various metaheuristic algorithms implemented in Python |
| metaheuristic-algorithms-python 0.1.3 | 2 | Various metaheuristic algorithms implemented in Python |
| metaheuristic-algorithms-python 0.1.4 | 2 | Various metaheuristic algorithms implemented in Python |
| metaheuristic-algorithms-python 0.1.5 | 2 | Various metaheuristic algorithms implemented in Python |
| metaheuristic-algorithms-python 0.1.6 | 2 | Various metaheuristic algorithms implemented in Python |
| mldatalib 0.2.1 | 2 | Library for data analysis - extracting, storing and retrieving features |
| mozsci 0.9.2 | 2 | Data science tools from Moz |
| mrec 0.3.0 | 2 | mrec recommender systems library |
| nimfa 1.1 | 2 | A Python Library for Nonnegative Matrix Factorization Techniques |
| nimfa 1.2.2 | 2 | A Python module for nonnegative matrix factorization |
| nipype 0.10.0 | 2 | Neuroimaging in Python: Pipelines and Interfaces |
| nipype 0.11.0rc1 | 2 | Neuroimaging in Python: Pipelines and Interfaces |
| nipype 0.11.0 | 2 | Neuroimaging in Python: Pipelines and Interfaces |
| nolearn 0.5 | 2 | scikit-learn compatible wrappers for neural net libraries, and other utilities. |
| nupic 0.3.0 | 2 | Numenta Platform for Intelligent Computing |
| nupic 0.3.1 | 2 | Numenta Platform for Intelligent Computing |
| nupic 0.3.2 | 2 | Numenta Platform for Intelligent Computing |
| nupic 0.3.3 | 2 | Numenta Platform for Intelligent Computing |
| nupic 0.3.4 | 2 | Numenta Platform for Intelligent Computing |
| nupic 0.3.5 | 2 | Numenta Platform for Intelligent Computing |
| nupic 0.3.6 | 2 | Numenta Platform for Intelligent Computing |
| osprey 0.4 | 2 | |Build Status| |PyPi version| |Supported Python versions| |License| |Documentation Status| |
| palladium 1.0 | 2 | Framework for setting up predictive analytics services |
| pave 0.69 | 2 | Simple push-based configuration and deployment tool, leveraging fabric. No servers, few dependencies. |
| pcalg 0.1.4 | 2 | CPDAG Estimation using PC-Algorithm |
| pepdata 0.6.5 | 2 | Immunological peptide datasets and amino acid properties |
| pepdata 0.6.7 | 2 | Immunological peptide datasets and amino acid properties |
| plone.testing 4.0.15 | 2 | Testing infrastructure for Zope and Plone projects. |
| ppsqlviz 1.0.1 | 2 | A command line visualization utility for SQL using Pandas library in Python. |
| PredictionIO 0.9.2 | 2 | PredictionIO Python SDK |
| PredictionIO 0.9.8 | 2 | PredictionIO Python SDK |
| propagate 0.2.1 | 2 | Propagation belief graph algorithm |
| py-mcmc 0.0a1 | 2 | A python module implementing some generic MCMC routines |
| pyarm 1.1.dev1 | 2 | A robotic arm model and simulator. |
| pyclust 0.1.3 | 2 | |
| pycv 0.2.2 | 2 | PyCV - A Computer Vision Package for Python Incorporating Fast Training of Face Detection |
| pydriver 1.0 | 2 | A framework for training and evaluating object detectors and classifiers in road traffic environment. |
| pydriver 1.0.1 | 2 | A framework for training and evaluating object detectors and classifiers in road traffic environment. |
| pyhacrf 0.0.12 | 2 | Hidden alignment conditional random field, a discriminative string edit distance |
| pyhacrf 0.1.1 | 2 | Hidden alignment conditional random field, a discriminative string edit distance |
| pyhacrf 0.1.2 | 2 | Hidden alignment conditional random field, a discriminative string edit distance |
| pymus 0.2.0 | 2 | Tools for audio analysis, special focus on score-informed audio analysis of instrumental / vocal solo recordings |
| pymus 0.2.1 | 2 | Tools for audio analysis, special focus on score-informed audio analysis of instrumental / vocal solo recordings |
| pytosca 0.2.1 | 2 | Application topologies using OASIS TOSCA YAML Profile |
| PyWeka 0.5dev | 2 | PyWeka, a python WEKA wrapper. |
| regex4dummies 1.4.2 | 2 | A NLP library that simplifies pattern finding in strings |
| regex4dummies 1.4.3 | 2 | A NLP library that simplifies pattern finding in strings |
| regex4dummies 1.4.4 | 2 | A NLP library that simplifies pattern finding in strings |
| regex4dummies 1.4.5 | 2 | A NLP library that simplifies pattern finding in strings |
| revrand 0.1rc1 | 2 | A library of scalable Bayesian generalised linear models with fancy features |
| revscoring 0.6.1 | 2 | A set of utilities for generating quality scores for MediaWiki revisions |
| revscoring 0.6.3 | 2 | A set of utilities for generating quality scores for MediaWiki revisions |
| revscoring 0.6.4 | 2 | A set of utilities for generating quality scores for MediaWiki revisions |
| revscoring 0.6.5 | 2 | A set of utilities for generating quality scores for MediaWiki revisions |
| revscoring 0.6.6 | 2 | A set of utilities for generating quality scores for MediaWiki revisions |
| revscoring 0.6.7 | 2 | A set of utilities for generating quality scores for MediaWiki revisions |
| revscoring 0.7.0 | 2 | A set of utilities for generating quality scores for MediaWiki revisions |
| revscoring 0.7.2 | 2 | A set of utilities for generating quality scores for MediaWiki revisions |
| revscoring 0.7.3 | 2 | A set of utilities for generating quality scores for MediaWiki revisions |
| revscoring 0.7.7 | 2 | A set of utilities for generating quality scores for MediaWiki revisions |
| revscoring 0.7.8 | 2 | A set of utilities for generating quality scores for MediaWiki revisions |
| revscoring 0.7.10 | 2 | A set of utilities for generating quality scores for MediaWiki revisions |
| revscoring 0.7.11 | 2 | A set of utilities for generating quality scores for MediaWiki revisions |
| root_numpy 4.3.0 | 2 | An interface between ROOT and NumPy |
| root_numpy 4.4.0 | 2 | An interface between ROOT and NumPy |
| rrbob 1.0 | 2 | Basic example of a Reproducible Research Project in Python/Bob |
| salve 2.4.1 | 2 | SALVE Configuration Deployment Language |
| ScalaFunctional 0.4.0 | 2 | Package for creating data pipelines, LINQ, and chain functional programming |
| ScalaFunctional 0.4.1 | 2 | Package for creating data pipelines, LINQ, and chain functional programming |
| scikit-gpuppy 0.9.3 | 2 | Gaussian Process Uncertainty Propagation with PYthon |
| sciluigi 0.9.2b3 | 2 | Helper library for writing dynamic, flexible workflows in luigi |
| simpleai 0.7.11 | 2 | An implementation of AI algorithms based on aima-python |
| skl-groups 0.1.5 | 2 | Addon to scikit-learn for handling set-based data. |
| skl-groups 0.1.6 | 2 | Addon to scikit-learn for handling set-based data. |
| skl-groups-accel 0.1.5 | 2 | Compiled components to speed up skl-groups. |
| skl-groups-accel 0.1.6 | 2 | Compiled components to speed up skl-groups. |
| sklearn-pandas 0.0.10 | 2 | Pandas integration with sklearn |
| sklearn-pandas 0.0.12 | 2 | Pandas integration with sklearn |
| sklearn-pandas 1.0.0 | 2 | Pandas integration with sklearn |
| sklearn-pandas 1.1.0 | 2 | Pandas integration with sklearn |
| spear.nist_sre12 1.0.0 | 2 | Speaker recognition toolchain for NIST SRE 2012 |
| storlever 0.1.2 | 2 | Management/Configure System for network and storage resource in linux system, with RESTful API |
| storm 0.20 | 2 | Storm is an object-relational mapper (ORM) for Python developed at Canonical. |
| strum 0.0 | 2 | Structured Prediction (SEARN and DAgger) |
| tabtool 0.2.0 | 2 | Utility to operate with tab separated files |
| tabtools 0.3.3 | 2 | Utility to operate with tab separated files |
| talon 1.0.7 | 2 | Mailgun library to extract message quotations and signatures. |
| talon 1.0.8 | 2 | Mailgun library to extract message quotations and signatures. |
| talon 1.0.9 | 2 | Mailgun library to extract message quotations and signatures. |
| talon 1.1.0 | 2 | Mailgun library to extract message quotations and signatures. |
| talon 1.2.1 | 2 | Mailgun library to extract message quotations and signatures. |
| treeCl 0.0.5 | 2 | Phylogenetic Clustering Package |
| treeCl 0.1.0 | 2 | Phylogenetic Clustering Package |
| trustedanalytics 0.4.0.post201509188625 | 2 | trusted analytics Toolkit build ID #BUILD_NUMBER# |
| trustedanalytics 0.4.0.post201509238678 | 2 | trusted analytics Toolkit build ID #BUILD_NUMBER# |
| trustedanalytics 0.4.0.post201509228667 | 2 | trusted analytics Toolkit build ID #BUILD_NUMBER# |
| trustedanalytics 0.4.1.post201509248706 | 2 | Trusted Analytics Toolkit |
| trustedanalytics 0.4.2.dev201510309170 | 2 | Trusted Analytics Toolkit |
| trustedanalytics 0.4.2.dev201511059264 | 2 | Trusted Analytics Toolkit |
| trustedanalytics 0.4.2.dev201511059271 | 2 | Trusted Analytics Toolkit |
| trustedanalytics 0.4.2.dev201511099323 | 2 | Trusted Analytics Toolkit |
| trustedanalytics 0.4.2.dev201512019643 | 2 | Trusted Analytics Toolkit |
| trustedanalytics 0.4.2.dev201512099825 | 2 | Trusted Analytics Toolkit |
| trustedanalytics 0.4.2.dev201512149874 | 2 | Trusted Analytics Toolkit |
| trustedanalytics 0.4.2.dev201512159888 | 2 | Trusted Analytics Toolkit |
| trustedanalytics 0.4.2.dev201512179907 | 2 | Trusted Analytics Toolkit |
| trustedanalytics 0.4.2.dev201512189955 | 2 | Trusted Analytics Toolkit |
| waterworks 0.2.5 | 2 | waterworks: Because everyone has their own utility library |
| xbob.buildout 1.0.3 | 2 | zc.buildout recipes to perform a variety of tasks required by Bob satellite packages |
| xbob.buildout 1.0.4 | 2 | zc.buildout recipes to perform a variety of tasks required by Bob satellite packages |
| xbob.daq 1.0.6 | 2 | Data-Acquisition Extension for Bob-based Applications |
| xbob.db.nist_sre12 1.2.0 | 2 | Speaker verification protocol on the NIST SRE 2012 |
| xbob.db.utfvp 2.0.0 | 2 | UTFVP Database Access API for Bob |
| xbob.db.voxforge 0.1.0 | 2 | Speaker verification protocol on a subset of the VoxForge database |
| xbob.fingervein 1.0.0 | 2 | Fingervein recognition based on Bob and the facereclib |
| xbob.flandmark 1.1.0 | 2 | Python bindings to the flandmark keypoint localization library |
| xbob.optflow.liu 1.1.2 | 2 | Python bindings to the optical flow framework by C. Liu |
| xbob.paper.BIOSIG2014 1.0.0 | 2 | Running the experiments as given in paper: "On the Vulnerability of Finger Vein Recognition to Spoofing". |
| xbob.paper.BTFS2013 1.0.1 | 2 | On the Improvements of Uni-modal and Bi-modal Fusions of Speaker and Face Recognition for Mobile Biometrics |
| xbob.paper.example 0.2.0 | 2 | Example of an article using Bob for reproducible experiments |
| xbob.paper.jmlr2013 0.2.0 | 2 | Example of an article using Bob for reproducible experiments |
| xbob.thesis.elshafey2014 0.0.1a0 | 2 | Experiments of Laurent El Shafey's Ph.D. thesis |
| xfacereclib.extension.CSU 2.0.0 | 2 | Wrapper classes to use the PythonFaceEvaluation classes from the CSU Face Recognition Resources |
| xfacereclib.paper.IET2014 1.0.0 | 2 | Running the experiments as given in paper: |
| xframes 0.2.8 | 2 | XFrame data manipulation for Spark. |
| xgboost 0.4a13 | 6 | XGBoost: eXtreme Gradient Boosting library. Contributors: https://github.com/dmlc/xgboost/blob/master/CONTRIBUTORS.md |
| zipline 0.7.0 | 2 | A backtester for financial algorithms. |
| zipline 0.8.0 | 2 | A backtester for financial algorithms. |
| zipline 0.8.2 | 2 | A backtester for financial algorithms. |
| zipline 0.8.3 | 2 | A backtester for financial algorithms. |
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