Description:

Python module to ease pattern classification analyses of large datasets. It provides high-level abstraction of typical processing steps (e.g. data preparation, classification, feature selection, generalization testing), a number of implementations of some popular algorithms (e.g. kNN, Ridge Regressions, Sparse Multinomial Logistic Regression, GPR. RFE, I-RELIEF), and bindings to external ML libraries (libsvm, shogun, R). While it is not limited to neuroimaging data (e.g. FMRI) it is eminently suited for such datasets.

It is actively developed project, thus you might better off trying it from the version control system. Please see documentation on how to obtain and "build" from sources.

Changes to previous version:

Initial Announcement on mloss.org.

BibTeX Entry: Download

Corresponding Paper BibTeX Entry: Download

Supported Operating Systems: Agnostic

Data Formats: None

Tags: Shogun, Python, Eeg, Classification, Regression, Support Vector Machines, K Nearest Neighbor Classification, Pca, Rfe, Neuroscience, Fmri, Framework, Gpr, Lars, Smlr, Meg

Archive: download here

Comments

Yaroslav Halchenko (on May 18, 2008, 17:07:37)

It is actively developed project at the moment, thus it is preferable to don't rely on releases but rather use master branch of git repository mentioned on the project homepage

Yaroslav Halchenko (on September 8, 2009, 20:21:46)

0.4.3 release update

Yaroslav Halchenko (on September 8, 2009, 20:29:35)

updated entry to don't be treated as PRE

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