python - Train a SVM (Support Vector Machine) classifier with Scikit-learn -

i want train different classifier using scikit-learn following code multi-label classification problem:

names = [     "nearest neighbors",     "linear svm", "rbf svm", "gaussian process",     "decision tree", "random forest", "neural net", "adaboost",     "naive bayes", "qda"]  classifiers = [     kneighborsclassifier(3),     svc(c=0.025),     svc(gamma=2, c=1),     gaussianprocessclassifier(1.0 * rbf(1.0)),     decisiontreeclassifier(max_depth=5),     randomforestclassifier(max_depth=5),     mlpclassifier(alpha=0.5),     adaboostclassifier(),     gaussiannb(),     quadraticdiscriminantanalysis()]  name, clf in izip(names, classifiers):     clf.fit(x_train, y_train)     score = clf.score(x_train, y_test)     print name, score 

kneighbors classifier works when reach svm classifier throws following exception:

traceback (most recent call last):   file "/users/mac/pycharmprojects/graphlstm/classifier.py", line 87, in <module>     clf.fit(x_train, y_train)   file "/library/python/2.7/site-packages/sklearn/svm/base.py", line 151, in fit     x, y = check_x_y(x, y, dtype=np.float64, order='c', accept_sparse='csr')   file "/library/python/2.7/site-packages/sklearn/utils/validation.py", line 526, in check_x_y     y = column_or_1d(y, warn=true)   file "/library/python/2.7/site-packages/sklearn/utils/validation.py", line 562, in column_or_1d     raise valueerror("bad input shape {0}".format(shape)) valueerror: bad input shape (9280, 39) 

what's reason , how can fix that?

edit: commented @vivek following classifier allowed multi-label classification:

sklearn.tree.decisiontreeclassifier sklearn.tree.extratreeclassifier sklearn.ensemble.extratreesclassifier sklearn.neighbors.kneighborsclassifier sklearn.neural_network.mlpclassifier sklearn.neighbors.radiusneighborsclassifier sklearn.ensemble.randomforestclassifier sklearn.linear_model.ridgeclassifiercv 

since multi-label classification problem, not estimators in scikit able handle them inherently. documentation provides list of estimators support multi-label out of box various tree based estimators or others :

sklearn.tree.decisiontreeclassifier sklearn.tree.extratreeclassifier sklearn.ensemble.extratreesclassifier sklearn.neighbors.kneighborsclassifier ... ... 

however there strategies one-vs-all can employed train required estimator (which doesn't support multilabel directly). sklearn estimator onevsrestclassifier made this.

see documentation here more details it.