machine learning - Class_weight for SVM classifier in Python -


i have set of parameters choose best ones svm.svc classifier using gridsearchcv:

x=dataset.ix[:, dataset.columns != 'class'] y=dataset['class'] x_train, x_test, y_train, y_test = cross_validation.train_test_split(x, y, test_size=0.5)  clf=svm.svc() params=         {'kernel':['linear', 'rbf', 'poly', 'sigmoid'],          'c':[1, 5, 10],          'degree':[2,3],          'gamma':[0.025, 1.0, 1.25, 1.5, 1.75, 2.0],          'coef0':[2, 5, 9],          'class_weight': [{1:10}, 'balanced']}  searcher = gridsearchcv(clf, params, cv=9, n_jobs=-1, scoring=f1) searcher.fit(x_train, y_train)

and error: valueerror: class_weight must dict, 'auto', or none, got: 'balanced' why have it, if in instructions of svm parameters there 'balanced', not 'auto' ?

'balanced' should working can see in line 51 or 74 https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/utils/class_weight.py

execute sklearn.__version__ check version running.


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