<p dir="auto">Hi, I am new to SVM and get a little confused when using SVC:</p>
<p dir="auto">In the <a href="http://scikit-learn.org/stable/modules/generated/sklearn.svm.SVC.html" rel="nofollow">document</a>, its description says</p>
<blockquote>
<p dir="auto">The multiclass support is handled according to a one-vs-one scheme.</p>
</blockquote>
<p dir="auto">while in input argument fields, it says</p>
<blockquote>
<p dir="auto">decision_function_shape : ‘ovo’, ‘ovr’, default=’ovr’</p>
</blockquote>
<p dir="auto">I personally tried both ovo and ovr with linear kernel, expected to get a large difference in training time, but got similar result. My data consists of 1694 classes with 512 features, and N instances per class (N=1~10). Theoretically, I expect the difference between O(n) and O(n^2) is significant.</p>
<p dir="auto">Is SVC by default uses ovr, but is fixed to use ovo when applying multi-class scenario? Changing decision_function_shape seems not working.</p>
<p dir="auto">The docs should probably state more cleary that decision_function_shape in SVC doesn't influence the multi-class strategy used in SVC but is only a hack to satisfy the sklearn API - and it should probably explain there or in the dev docs how it is done.</p>