On Jan 12, 2014, at 9:40 PM, Marius 't Hart wrote: > > My toy problem is to classify the two most extreme conditions (two targets) > based on averaged Cz and Pz activity (two features) for every 200ms epoch in > the preparation interval. An N-fold partitioner using a linear SVM performs > at chance at the start of the interval, and then reaches a plateau at about > 60% pretty quickly. Not spectacular maybe, but I was pretty excited to get > some results and hope it is good enough for what I want to do next. See > performance here: > http://www.mariusthart.net/images/LinSVM_Cz-Pz_c18_pp1-8.png
Did you provide an equal number of samples for each class (target)? Because if you didn't then 60% could, in principle, be due to chance. That is, if 60% of the samples are in one class in the training set, then a 'naive' classifier that is just guessing that class all the time will give 60% accuracy. Also: what preprocessing did you do? Any z-scoring, baseline correction etc? > > In the CNV plot it looked like the usefulness of Pz and Cz for the > classifier(s) should flip at around 1 second in the preparation interval, so > I wanted to look at sensitivity. [...] That doesn't look like what I expected > - but I find it hard to judge if what I'm doing is actually correct. For > example, on inspecting all the different sensitivity datasets, it looks like > the sign that each feature gets is usually the same... but there are a few > exceptions. Does the sign actually mean anything in terms of a feature's > usefulness? As far as I know the sign is not that important - it's more about the relative magnitude. If the sensitivity is further away from zero then that means the feature is more important for discriminating the classes. > > When I look at the individual sensitivity datasets, they look like this: > > In [19]: sensitivities[0] > Out[19]: Dataset(array([[ 0.14507451, -0.02963355]]), > sa=SampleAttributesCollection(items=[ArrayCollectable(name='targets', > doc=None, value=array([[8, 1]]), length=1), ArrayCollectable(name='biases', > doc='Sequence attribute', value=array([-0.63935516]), length=1)]), > fa=FeatureAttributesCollection(items=[]), > a=DatasetAttributesCollection(items=[])) > > Now there is a number -0.63935516 that puzzles me. Second noob question: what > does this 'bias' number mean? I'm not an expert on this... maybe it's the offset from the separating hyperplane, i.e. all vectors x for which dot(a,x)=b with b the bias and a the sensitivities? _______________________________________________ Pkg-ExpPsy-PyMVPA mailing list [email protected] http://lists.alioth.debian.org/cgi-bin/mailman/listinfo/pkg-exppsy-pymvpa
