Am 23.01.2013 18:39, schrieb Lars Buitinck: > > if you want more predictions or something... > More in detail: OneVsRestClassifier exports an object called > label_binarizer_, which is used to transform decision function values > D back to class labels. By default, it picks all the classes for which > D > 0, but its threshold argument can be used to change that. > > So, if clf is an OvR classifier and > > D = clf.decision_function(x) > > for a *single sample* x contains no positive values, then > > # untested, may contain mistakes > clf.label_binarizer_.inverse_transform(D, threshold=(D.max() + epsilon)) > > will predict at least one class label for x, namely the one with the > highest value according to the decision_function. The epsilon is > needed because inverse_transform compares values using >, not >=; set > it to a small value. Doing this for batches of samples is a bit more > involved. > > Of course, you can set the threshold to any value. Whether any of this > makes sense depends on your problem. > > [I used to be opposed to exporting the LabelBinarizer object on OvR > estimators, but I guess I should give up the struggle now -- this is > actually useful.] > I didn't even realize this possibility existed. I would have done it "by hand". Thanks for the instructions.
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