我的第一个多类分类。我有值X和Y.Y有5个值[0,1,2,3,4]。但我得到这个＆＃34;不支持多类格式＆＃34;。明白我需要xgb_params中的num_class，但是如果我想要＃num ;class＆＃39;：range（0,5,1）而不是获取估算器XGBClassifier的无效参数num_class。
xgb_model = xgb.XGBClassifier(objective='multi:softmax')
xgb_params = [
{
"n_estimators": range(50, 501, 50),
}
]
cv = cross_validation.StratifiedShuffleSplit(y_train, n_iter=5,
test_size=0.3, random_state=42)
xgb_grid = grid_search.GridSearchCV(xgb_model, xgb_params, scoring='roc_auc', cv=cv, n_jobs=-1, verbose=3)
xgb_grid.fit(X_train, y_train)
这个值的例子：
X Y
-1.35173485 1.50224188 2.04951167 0.43759658 0.24381777 2
2.81047260 1.31259056 1.39265240 0.16384002 0.65438366 3
2.32878809 -1.92845940 -2.06453246 0.73132270 0.11771229 2
-0.12810555 -2.07268765 -2.40760215 0.97855042 0.11144164 1
1.88682063 0.75792329 -0.09754671 0.46571931 0.62111648 2
-1.09361266 1.74758304 2.49960891 0.36679883 0.88895562 2
0.71760095 -1.30711698 -2.15681966 0.33700593 0.07171119 2
4.60060308 -1.60544855 -1.88996123 0.94500124 0.63776116 4
-0.84223064 2.78233537 3.07299711 0.31470071 0.34424704 1
-0.71236435 0.53140549 0.46677096 0.12320728 0.58829090 2
-0.35333909 1.12463059 1.70104349 0.89084673 0.16585229 2
3.04322100 -1.36878116 -2.31056167 0.81178387 0.04095645 1
-1.04088918 -1.97497570 -1.93285343 0.54101882 0.02528487 1
-0.41624939 0.54592833 0.95458283 0.40004902 0.55062705 2
-1.77706795 0.29061278 0.68186697 0.17430716 0.75095729 0
代码错误：
Fitting 5 folds for each of 10 candidates, totalling 50 fits
[CV] n_estimators=50 .................................................
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-213-43ea40d77391> in <module>()
10
11 xgb_grid = grid_search.GridSearchCV(xgb_model, xgb_params, scoring='roc_auc', cv=cv, n_jobs=-1, verbose=3)
---> 12 xgb_grid.fit(X_train, y_train)
/home/rudolf/anaconda2/lib/python2.7/site-packages/sklearn/grid_search.pyc in fit(self, X, y)
827
828 """
--> 829 return self._fit(X, y, ParameterGrid(self.param_grid))
830
831
/home/rudolf/anaconda2/lib/python2.7/site-packages/sklearn/grid_search.pyc in _fit(self, X, y, parameter_iterable)
571 self.fit_params, return_parameters=True,
572 error_score=self.error_score)
--> 573 for parameters in parameter_iterable
574 for train, test in cv)
575
/home/rudolf/anaconda2/lib/python2.7/site-packages/sklearn/externals/joblib/parallel.pyc in __call__(self, iterable)
756 # was dispatched. In particular this covers the edge
757 # case of Parallel used with an exhausted iterator.
--> 758 while self.dispatch_one_batch(iterator):
759 self._iterating = True
760 else:
/home/rudolf/anaconda2/lib/python2.7/site-packages/sklearn/externals/joblib/parallel.pyc in dispatch_one_batch(self, iterator)
606 return False
607 else:
--> 608 self._dispatch(tasks)
609 return True
610
/home/rudolf/anaconda2/lib/python2.7/site-packages/sklearn/externals/joblib/parallel.pyc in _dispatch(self, batch)
569 dispatch_timestamp = time.time()
570 cb = BatchCompletionCallBack(dispatch_timestamp, len(batch), self)
--> 571 job = self._backend.apply_async(batch, callback=cb)
572 self._jobs.append(job)
573
/home/rudolf/anaconda2/lib/python2.7/site-packages/sklearn/externals/joblib/_parallel_backends.pyc in apply_async(self, func, callback)
107 def apply_async(self, func, callback=None):
108 """Schedule a func to be run"""
--> 109 result = ImmediateResult(func)
110 if callback:
111 callback(result)
/home/rudolf/anaconda2/lib/python2.7/site-packages/sklearn/externals/joblib/_parallel_backends.pyc in __init__(self, batch)
324 # Don't delay the application, to avoid keeping the input
325 # arguments in memory
--> 326 self.results = batch()
327
328 def get(self):
/home/rudolf/anaconda2/lib/python2.7/site-packages/sklearn/externals/joblib/parallel.pyc in __call__(self)
129
130 def __call__(self):
--> 131 return [func(*args, **kwargs) for func, args, kwargs in self.items]
132
133 def __len__(self):
/home/rudolf/anaconda2/lib/python2.7/site-packages/sklearn/cross_validation.pyc in _fit_and_score(estimator, X, y, scorer, train, test, verbose, parameters, fit_params, return_train_score, return_parameters, error_score)
1682
1683 else:
-> 1684 test_score = _score(estimator, X_test, y_test, scorer)
1685 if return_train_score:
1686 train_score = _score(estimator, X_train, y_train, scorer)
/home/rudolf/anaconda2/lib/python2.7/site-packages/sklearn/cross_validation.pyc in _score(estimator, X_test, y_test, scorer)
1739 score = scorer(estimator, X_test)
1740 else:
-> 1741 score = scorer(estimator, X_test, y_test)
1742 if hasattr(score, 'item'):
1743 try:
/home/rudolf/anaconda2/lib/python2.7/site-packages/sklearn/metrics/scorer.pyc in __call__(self, clf, X, y, sample_weight)
169 y_type = type_of_target(y)
170 if y_type not in ("binary", "multilabel-indicator"):
--> 171 raise ValueError("{0} format is not supported".format(y_type))
172
173 if is_regressor(clf):
ValueError: multiclass format is not supported
答案 0 :(得分：4)
删除参数后，我有同样的错误：scoring='roc_auc'，它有效！
也许roc_auc仅用于二进制类
答案 1 :(得分：1)
roc_auc仅限于二进制分类问题。
接收机工作特性曲线（ROC）下的计算面积 AUC）。
注意：此实现仅限于二进制分类 标签指示符格式的任务或多标签分类任务。
参考：http://scikit-learn.org/stable/modules/generated/sklearn.metrics.roc_auc_score.html
答案 2 :(得分：0)
我也遇到了同样的问题，因为我曾经使用过'roc_auc'评分机制，而我曾经使用过'accuracy'，并且有效。
从sklearn.metrics导入precision_score 分数=准确度得分（y_test，喜欢）