neurosynth/analysis/classify.py
def fit(self, X, y, cv=None, class_weight='auto'):
        """ Fits X to outcomes y, using clf """

        # Incorporate error checking such as :
        # if isinstance(self.classifier, ScikitClassifier):
        #     do one thingNone
        # otherwiseNone.

        self.X = X
        self.y = y

        self.set_class_weight(class_weight=class_weight, y=y)

        self.clf = self.clf.fit(X, y)

        return self.clf
def fit(self, X, y, cv=None, class_weight='auto'):
        # Incorporate error checking such as :
        # if isinstance(self.classifier, ScikitClassifier):
        #     do one thingNone
        # otherwiseNone.

        self.X = X
        self.y = y

        self.set_class_weight(class_weight=class_weight, y=y)

        self.clf = self.clf.fit(X, y)

        return self.clf
def fit ( self , X , y , cv = None , class_weight = 'auto' ) : # Incorporate error checking such as : # if isinstance(self.classifier, ScikitClassifier): #     do one thingNone # otherwiseNone. self . X = X self . y = y self . set_class_weight ( class_weight = class_weight , y = y ) self . clf = self . clf . fit ( X , y ) return self . clf
Fits X to outcomes y, using clf
Fits X to outcomes y using clf