Hello all,
I have the following code:
. . . .
# 'train' is a (M,N) numpy array (input) and 'traint' is a (M,) numpy array
(target/label)
clf = SVC(kernel="rbf", C=1.74, gamma=0.0023, probability=True)
clf.fit(train, traint)
print clf.classes_ # Ensure our classes are [0,1]
t1 = clf.predict_proba(matrix)
t2 = clf.predict(matrix)
print 'probabilities: ', t1
print 'class: ' , t2
#repeating for various matrices..
. . . .
I suppose that t2 must be 0 when probability for the 1st class is bigger,
else 1
However the two results (t1,t2) are inconsistent
results:
[0 1]
probabilities: [[ 0.2629873 0.7370127]]
class: [0]
probabilities: [[ 0.22160846 0.77839154]]
class: [0]
*probabilities: [[ 0.17936026 0.82063974]] class: [1] *
probabilities: [[ 0.28899413 0.71100587]]
class: [0]
*probabilities: [[ 0.17035052 0.82964948]] class: [1] *
3rd and 5th examples agree, but the rest?
Any idea what's going on?
Thanks for your time
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