machine learning - OneVsRestClassifier(svm.SVC()).predict() gives continous values -

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i trying use y_scores=onevsrestclassifier(svm.svc()).predict() on datasets iris , titanic .the trouble getting y_scores continous values.like iris dataset getting :

[[ -3.70047231  -0.74209097   2.29720159]  [ -1.93190155   0.69106231  -2.24974856] .....

i using onevsrestclassifier other classifier models knn,randomforest,naive bayes , giving appropriate results in form of 

[[ 0  1   0]  [ 1   0  1]...

etc on iris dataset .please help.

well not true.

>>> sklearn.multiclass import onevsrestclassifier >>> sklearn.svm import svc >>> sklearn.datasets import load_iris >>> iris = load_iris() >>> clf = onevsrestclassifier(svc()) >>> clf.fit(iris['data'], iris['target']) onevsrestclassifier(estimator=svc(c=1.0, cache_size=200, class_weight=none, coef0=0.0, degree=3, gamma=0.0,   kernel='rbf', max_iter=-1, probability=false, random_state=none,   shrinking=true, tol=0.001, verbose=false),           n_jobs=1) >>> print clf.predict(iris['data']) [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0  0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1  1 1 1 2 1 1 1 1 1 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2  2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2  2 2]

maybe called decision_function instead (which match output dimension, predict supposed return vector, not matrix). then, svm returns signed distances each hyperplane, decision function mathematical perspective.


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