我在Python 2.7.6中运行scikit-learn（0.15）。
我的训练集（X）是33x3（33条记录，3个特征），目标（y）是33x1。我正在尝试在此数据集上使用learning_curve函数（来自sklearn.learning_curve import learning_curve）。
这是X的值：
array([[ 1. , -0.12493874, 0.04575749],
[ 0. , -0.30103 , 0.03140846],
[ 1. , -0.17609126, 0.11394335],
[ 1. , -0.30103 , -0.06694679],
[ 1. , -0.30103 , -0.12104369],
[ 1. , -0.23408321, 0.11270428],
[ 1. , 0.19188553, 0.22577904],
[ 1. , -0.23736092, -0.42100531],
[ 0. , 0.21085337, 0.13966199],
[ 1. , -0.06214791, 0.07716595],
[ 1. , 0.14612804, -0.01223446],
[ 1. , 0.1383027 , 0.1217336 ],
[ 1. , -0.30103 , -0.18073616],
[ 0. , 0.02996322, -0.09108047],
[ 0. , 0.05435766, 0.1638568 ],
[ 1. , -0.11394335, 0. ],
[ 1. , 0.06694679, 0.30998484],
[ 0. , 0.64345268, 0.02802872],
[ 1. , 0. , -0.01639042],
[ 0. , 0.11394335, -0.0234811 ],
[ 0. , 0. , 0.18799048],
[ 1. , 0. , 0.10914447],
[ 1. , -0.04139269, 0. ],
[ 0. , 0.18905624, 0.17026172],
[ 1. , -0.14132915, 0.15209098],
[ 0. , 0.30103 , 0.27036118],
[ 1. , 0.22184875, 0.05435766],
[ 0. , 0.34242268, 0.09455611],
[ 1. , -0.20411998, -0.1173856 ],
[ 0. , 0.11394335, 0.01189922],
[ 1. , -0.22184875, -0.01378828],
[ 1. , 0.13262557, 0.14390658],
[ 0. , 0.14612804, 0.13353891]])
对于y：
array([1, 0, 1, 1, 1, 1, 0, 1, 0, 0, 1, 0, 1, 1, 1, 0, 0, 0, 1, 0, 1, 1, 1,
0, 1, 0, 0, 0, 1, 0, 1, 0, 0])
当我进入时：
Gmodel=GaussianNB()
learning_curve(Gmodel,X,y,train_sizes=[3,6,10],cv=3)
我收到以下错误：我做错了什么？
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-143-f7cccd85f1fe> in <module>()
----> 1 learning_curve(Gmodel,train_all,target,train_sizes=[3,6,10],cv=3)
/Users/***/Library/Enthought/Canopy_64bit/User/lib/python2.7/site-packages/sklearn/learning_curve.pyc in learning_curve(estimator, X, y, train_sizes, cv, scoring, exploit_incremental_learning, n_jobs, pre_dispatch, verbose)
131 clone(estimator), X, y, scorer, train[:n_train_samples], test,
132 verbose, parameters=None, fit_params=None, return_train_score=True)
--> 133 for train, test in cv for n_train_samples in train_sizes_abs)
134 out = np.array(out)[:, :2]
135 n_cv_folds = out.shape[0] // n_unique_ticks
/Users/***/Library/Enthought/Canopy_64bit/User/lib/python2.7/site-packages/sklearn/externals/joblib/parallel.pyc in __call__(self, iterable)
642 self._iterating = True
643 for function, args, kwargs in iterable:
--> 644 self.dispatch(function, args, kwargs)
645
646 if pre_dispatch == "all" or n_jobs == 1:
/Users/***/Library/Enthought/Canopy_64bit/User/lib/python2.7/site-packages/sklearn/externals/joblib/parallel.pyc in dispatch(self, func, args, kwargs)
389 """
390 if self._pool is None:
--> 391 job = ImmediateApply(func, args, kwargs)
392 index = len(self._jobs)
393 if not _verbosity_filter(index, self.verbose):
/Users/***/Library/Enthought/Canopy_64bit/User/lib/python2.7/site-packages/sklearn/externals/joblib/parallel.pyc in __init__(self, func, args, kwargs)
127 # Don't delay the application, to avoid keeping the input
128 # arguments in memory
--> 129 self.results = func(*args, **kwargs)
130
131 def get(self):
/Users/***/Library/Enthought/Canopy_64bit/User/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)
1195 start_time = time.time()
1196
-> 1197 X_train, y_train = _safe_split(estimator, X, y, train)
1198 X_test, y_test = _safe_split(estimator, X, y, test, train)
1199 if y_train is None:
/Users/***/Library/Enthought/Canopy_64bit/User/lib/python2.7/site-packages/sklearn/cross_validation.pyc in _safe_split(estimator, X, y, indices, train_indices)
1245
1246 if y is not None:
-> 1247 y_subset = y[safe_mask(y, indices)]
1248 else:
1249 y_subset = None
TypeError: only integer arrays with one element can be converted to an index