Added coactivation map example
bug fix in setting manual classifier
Merge branch 'master' of https://github.com/neurosynth/neurosynth
added extremely randomized forest trees as method type. it seems to work better and supports feature importance so i think i might focus on this algorithm
Fixes #32
Updated README
When using GridSearchCV you can get back a trained clf even w/ nested cross-validation
This is not yet possible using a regular CVed clf
cosmetic changes and added ability to return summary + clf (to be able to get class n's)
custom classifier fix
made the actual extraction of data a different function so user can get data themselves if they want to do something custom (get_studies_by_regions). classify_regions class this. improves docstring and added gradient boosting
created .gitignore to exclude .pyc and other unnecessary files
bug in Classifier such that if using non GridSearch cv, classifier returned is not already fitted. Thus, commented out fitting
Added feature to dataset.get_feature_names such that a reduced subset of feature names can be given. The result is the features given in the ORDER that they appear the feature table
bug in classify
moved regularization out of the class because you have to pass data in anyways
added NITP2013 demos
dataset load instead of pickle in meta analysis example
minor fixes
added thresholding option when decoding
Added ability to specify scoring function in classification