I use the following codes to obtain decision values for SVC classifier clf.
-----------------------------------------------------------------------------------------------
In [5]: >>> clf = svm.SVC()
In [23]: >>> X = [[0], [1], [2]]
In [24]: >>> Y = [0, 1, 2]
In [25]: clf.fit(X, Y)
Out[25]:
SVC(C=1.0, cache_size=200, coef0=0.0, degree=3, gamma=1.0, kernel='rbf',
probability=False, scale_C=False, shrinking=True, tol=0.001)
In [26]: clf.predict([[0]])
Out[26]: array([ 0.])
In [27]: clf.predict(X)
Out[27]: array([ 0., 1., 2.])
In [28]: clf.decision_function(X)
Out[28]:
*array([[-0.63212056, -0.98168436, -0.3495638 ],*
* [ 0.63212056, -0. , -0.63212056],*
* [ 0.3495638 , 0.98168436, 0.63212056]])*
*
*
-----------------------------------------------------------------------------------------------
The decision_function return confusing results. Why *[-0.63212056,
-0.98168436, -0.3495638 ] *corresponds to label 0 ?
The encoding of labels seems to be different from the natural orders of (0,
1, 2 ...) .
After reading the README file of LibSVM, I found the label encoding can be
obtained by calling svm_get_labels().
Where can I find this function wrapper in sklearn? Without that, the return
results of decision_function() are difficult to interpret.
Thanks!
--
Best Wishes
--------------------------------------------
Meng Xinfan（蒙新泛）
Institute of Computational Linguistics
Department of Computer Science & Technology
School of Electronic Engineering & Computer Science
Peking University
Beijing, 100871
China
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