I've trained a GNB classifier on a dataset with moderate success and would like to look at the regions that the classifier finds most interesting. I notice in the code that there is a sensitivity analyser for the GNB module but that it has been removed:
# XXX Later come up with some # could be a simple t-test maps using distributions # per each class #def get_sensitivity_analyzer(self, **kwargs): # """Returns a sensitivity analyzer for GNB.""" # return GNBWeights(self, **kwargs) # XXX Is there any reason to use properties? #means = property(lambda self: self.__biases) #variances = property(lambda self: self.__weights) ## class GNBWeights(Sensitivity): ## """`SensitivityAnalyzer` that reports the weights GNB trained ## on a given `Dataset`. ## """ ## _LEGAL_CLFS = [ GNB ] ## def _call(self, dataset=None): ## """Extract weights from GNB classifier. ## GNB always has weights available, so nothing has to be computed here. ## """ ## clf = self.clf ## means = clf.means ## XXX we can do something better ;) ## return mean Is the use of the means considered to be poor in some sense? Could anyone provide more information about this: # XXX Later come up with some # could be a simple t-test maps using distributions # per each class Thanks, Tom
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