我正在尝试使用GridSearch来估计LinearSVC（）的参数，如下所示-
clf_SVM = LinearSVC()
params = {
'C': [0.5, 1.0, 1.5],
'tol': [1e-3, 1e-4, 1e-5],
'multi_class': ['ovr', 'crammer_singer'],
}
gs = GridSearchCV(clf_SVM, params, cv=5, scoring='roc_auc')
gs.fit(corpus1, y)
corpus1具有形状（17267001），y具有形状（17.26，）
这是一个多类分类，y的值从0到3，包括0和3，即有四个类。
但这给了我以下错误-
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-220-0c627bda0543> in <module>()
5 }
6 gs = GridSearchCV(clf_SVM, params, cv=5, scoring='roc_auc')
----> 7 gs.fit(corpus1, y)
/usr/local/lib/python2.7/dist-packages/sklearn/grid_search.pyc in fit(self, X, y)
594
595 """
--> 596 return self._fit(X, y, ParameterGrid(self.param_grid))
597
598
/usr/local/lib/python2.7/dist-packages/sklearn/grid_search.pyc in _fit(self, X, y, parameter_iterable)
376 train, test, self.verbose, parameters,
377 self.fit_params, return_parameters=True)
--> 378 for parameters in parameter_iterable
379 for train, test in cv)
380
/usr/local/lib/python2.7/dist-packages/sklearn/externals/joblib/parallel.pyc in __call__(self, iterable)
651 self._iterating = True
652 for function, args, kwargs in iterable:
--> 653 self.dispatch(function, args, kwargs)
654
655 if pre_dispatch == "all" or n_jobs == 1:
/usr/local/lib/python2.7/dist-packages/sklearn/externals/joblib/parallel.pyc in dispatch(self, func, args, kwargs)
398 """
399 if self._pool is None:
--> 400 job = ImmediateApply(func, args, kwargs)
401 index = len(self._jobs)
402 if not _verbosity_filter(index, self.verbose):
/usr/local/lib/python2.7/dist-packages/sklearn/externals/joblib/parallel.pyc in __init__(self, func, args, kwargs)
136 # Don't delay the application, to avoid keeping the input
137 # arguments in memory
--> 138 self.results = func(*args, **kwargs)
139
140 def get(self):
/usr/local/lib/python2.7/dist-packages/sklearn/cross_validation.pyc in _fit_and_score(estimator, X, y, scorer, train, test, verbose, parameters, fit_params, return_train_score, return_parameters)
1238 else:
1239 estimator.fit(X_train, y_train, **fit_params)
-> 1240 test_score = _score(estimator, X_test, y_test, scorer)
1241 if return_train_score:
1242 train_score = _score(estimator, X_train, y_train, scorer)
/usr/local/lib/python2.7/dist-packages/sklearn/cross_validation.pyc in _score(estimator, X_test, y_test, scorer)
1294 score = scorer(estimator, X_test)
1295 else:
-> 1296 score = scorer(estimator, X_test, y_test)
1297 if not isinstance(score, numbers.Number):
1298 raise ValueError("scoring must return a number, got %s (%s) instead."
/usr/local/lib/python2.7/dist-packages/sklearn/metrics/scorer.pyc in __call__(self, clf, X, y)
136 y_type = type_of_target(y)
137 if y_type not in ("binary", "multilabel-indicator"):
--> 138 raise ValueError("{0} format is not supported".format(y_type))
139
140 try:
ValueError: multiclass format is not supported
删除scoring='roc_auc'，它将起作用，因为roc_auc曲线不支持分类数据。
来自：
http://scikit-learn.org/stable/modules/generated/sklearn.metrics.roc_auc_score.html#sklearn.metrics.roc_auc_score
"注意：此实现仅限于标签指示符格式的二进制分类任务或多标签分类任务。"
尝试：
from sklearn import preprocessing
y = preprocessing.label_binarize(y, classes=[0, 1, 2, 3])
在你训练之前。这将对你的y.进行"一次热"编码
如前所述，必须首先对y 进行二进制化
y = label_binarize(y, classes=[0, 1, 2, 3])
然后使用像CCD_ 4或CCD_。例如：
clf_SVM = OneVsRestClassifier(LinearSVC())
params = {
'estimator__C': [0.5, 1.0, 1.5],
'estimator__tol': [1e-3, 1e-4, 1e-5],
}
gs = GridSearchCV(clf_SVM, params, cv=5, scoring='roc_auc')
gs.fit(corpus1, y)
对于多类分类，您必须在roc_auc_score函数中指定一些参数：multi_class和average（可选）。请参阅文档。
但是如何将这些参数放置在GridSearchCV中呢？为此，应使用make_scorer函数。
multi_roc = make_scorer(roc_auc_score, average='weighted', multi_class='ovr', needs_proba=True)
searcher = GridSearchCV(LogisticRegression(n_jobs=1), params, scoring=multi_roc, cv=skf, verbose=3, n_jobs=16)
请参阅文档和其他问题。
根据您的问题，您可以直接使用to_categorical而不是 preprocessing.label_binarize()。问题实际上来自于使用得分=roc_auc。请注意，roc_auc不支持分类数据。