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scikit learn

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  Analyzed 1 day ago

Python module integrating various machine learning algorithms under a common interface. It offers a wide range of methods such as Support Vector Machines, linear models (L1, L2 penalized), logistic regression, gaussian mixture models and more. The large number of algorithms aleady implemented allows ... [More] for easy comparison of accuracy and performance of various algorithms. [Less]

191K lines of code

286 current contributors

3 months since last commit

63 users on Open Hub

High Activity
5.0
 
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SHOGUN

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  Analyzed about 1 month ago

The SHOGUN machine learning toolbox's focus is on large scale kernel methods and especially on Support Vector Machines (SVM). It comes with a generic interface for SVMs, features several SVM and kernel implementations, includes LinAdd optimizations and also Multiple Kernel Learning algorithms. ... [More] SHOGUN also implements a number of linear methods. It allows the input feature-objects to be dense, sparse or strings and of type int/short/double/char. It provides efficient implementations several kernels but also linear methods, hidden markov models etc. and interfaces to matlab,octave,python,R and has a cmdline interface and allows C++ extensions via a library. [Less]

295K lines of code

34 current contributors

about 1 month since last commit

11 users on Open Hub

Activity Not Available
5.0
 
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PyBrain

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  Analyzed about 2 months ago

PyBrain is a modular Machine Learning Library for Python. It's goal is to offer flexible, easy-to-use yet still powerful algorithms for Machine Learning Tasks and a variety of predefined environments to test and compare your algorithms. PyBrain is short for Python-Based Reinforcement Learning ... [More] , Artificial Intelligence and Neural Network Library. It's the Swiss army knife for machine learning and neural networking. [Less]

36K lines of code

0 current contributors

about 1 year since last commit

6 users on Open Hub

Activity Not Available
5.0
 
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randomforest-matlab

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  Analyzed about 1 year ago

This is a Matlab (and Standalone application) port for the excellent machine learning algorithm `Random Forests' - By Leo Breiman et al. from the R-source by Andy Liaw et al. http://cran.r-project.org/web/packages/randomForest/index.html ( Fortran original by Leo Breiman and Adele Cutler, R port by ... [More] Andy Liaw and Matthew Wiener.) Current code version is based on 4.5-29 from source of randomForest package. I especially am grateful for all the help i got from Andy Liaw. This project would not have been possible if not for the previous code by Andy Liaw, Matthew Wiener, Leo Brieman, Adele Cutler. The wiki has short articles on using rfImpute to input in missing values and basic installation procedures. 1-march-2010 Bug: Note the inputs to the package are in double. So make sure you are [Less]

4.96K lines of code

0 current contributors

about 4 years since last commit

2 users on Open Hub

Activity Not Available
0.0
 
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PPI Benchmark

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  Analyzed almost 4 years ago

A comprehensive benchmark of kernel methods to extract protein-protein interactions from literature.

721K lines of code

2 current contributors

over 4 years since last commit

1 users on Open Hub

Activity Not Available
0.0
 
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