Do you mean that you're getting better performance when you're just
leaving out one trial instead of one run?
If so, How many runs? How many examples per run? Is everything fully
balanced (equal number of training examples in each class) under each
cross-validation scheme?
Jo
On 9/23/2015 3:03 PM, Raúl Hernández wrote:
Hi, I’m trying to evaluate on trial by trial basis how well a region can
predict the stimulus being presented to compare it with the
participant’s judgment of the stimulus. So I’m training the classifier
with data from all the trials on all the runs except by the one that I
want to predict.
I’m getting really good classifications better than when I was
predicting one run using all the others. Supposedly it should be a
little better as I’m training with a little more data but I’m worried
I’m doing something wrong.
Could anyone let me know if I’m making some sort of mistake?
I know that there should be a more efficient way to do it but I wanted
something easy, this is my code:
predictions = [] #this is a vector that will contain the predictions of
the classifier
for i,dsTest in enumerate(ds): #go through all the trials on ds and
separate one to test
clf = LinearCSVMC()
fclf = FeatureSelectionClassifier(clf, fsel)
dsTrain = []
dsTrain.append(ds[0:i]) #separates the training data
dsTrain.append(ds[i:-1])
dsTrain = vstack(dsTrain) #stacks it
fclf.train(dsTrain)
predicted = fclf.predict(dsTest) #stores the prediction
predictions.append(dsTest.targets == predicted) #checks whether the
prediction was correct
print np.mean(predictions) #checks the mean -accuracy of all predictions
I would really appreciate any feedback, thanks!
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Joset A. Etzel, Ph.D.
Research Analyst
Cognitive Control & Psychopathology Lab
Washington University in St. Louis
http://mvpa.blogspot.com/
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