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) providing additional learning methods (e.g. SVM). While it is not limited to neuroimaging data (e.g. FMRI) it is eminently suited for such datasets.
Homepage: http://www.pymvpa.org |
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