由于我第一次使用python进行数据挖掘，我面临着调整参数和获得最佳参数值（cutoff，classwt，sampsize）的问题 . 我试图在scikit中使用随机森林找到不同类的截止值 . 我正在使用以下代码

def cutoff_predict(rf,trainArr,cutoff):
   return (rf.predict_prob(trainArr)[:,1]>cutoff).astype(int)

score=[]
def custom_f1(cutoff):
    def f1_cutoff(rf,trainArr,y):
        ypred=cutoff_predict(rf,trainArr,cutoff)
        return sklearn.metrics.f1_score(Actualres,results)
    return f1_cutoff
for cutoff in np.arange(0.1,0.9,0.1):
    rf = RandomForestClassifier(n_estimators=100) #Random forest generation for Classification
    rf.fit(trainArr, trainRes) #Fit the random forest model
validated=cross_val_score(rf,trainArr,trainRes,cv=10,scoring=custom_f1(cutoff))
    score.append(validated)

但我收到以下错误 .

IndexError                                Traceback (most recent call last)
<ipython-input-14-f8b808ce9a4d> in <module>()
     94     rf = RandomForestClassifier(n_estimators=100) #Random forest generation for Classification
     95     rf.fit(trainArr, trainRes) #Fit the random forest model
---> 96     validated=cross_val_score(rf,trainArr,trainRes,cv=10,scoring=custom_f1(cutoff))
     97     score.append(validated)

C:\Python27\Anaconda\lib\site-packages\sklearn\cross_validation.pyc in cross_val_score(estimator, X, y, scoring, cv, n_jobs, verbose, fit_params, pre_dispatch)
   1350     X, y = indexable(X, y)
   1351 
-> 1352     cv = _check_cv(cv, X, y, classifier=is_classifier(estimator))
   1353     scorer = check_scoring(estimator, scoring=scoring)
   1354     # We clone the estimator to make sure that all the folds are

C:\Python27\Anaconda\lib\site-packages\sklearn\cross_validation.pyc in _check_cv(cv, X, y, classifier, warn_mask)
   1604         if classifier:
   1605             if type_of_target(y) in ['binary', 'multiclass']:
-> 1606                 cv = StratifiedKFold(y, cv, indices=needs_indices)
   1607             else:
   1608                 cv = KFold(_num_samples(y), cv, indices=needs_indices)

C:\Python27\Anaconda\lib\site-packages\sklearn\cross_validation.pyc in __init__(self, y, n_folds, indices, shuffle, random_state)
    432         for test_fold_idx, per_label_splits in enumerate(zip(*per_label_cvs)):
    433             for label, (_, test_split) in zip(unique_labels, per_label_splits):
--> 434                 label_test_folds = test_folds[y == label]
    435                 # the test split can be too big because we used
    436                 # KFold(max(c, self.n_folds), self.n_folds) instead of

IndexError: too many indices for array

这可能是什么问题？另外：在'R'中我们可以选择调整'cutoff'参数（cutoff = 1 /（类数）） . 在随机森林中有一个类似的参数（scikit学习包）来调整python吗？

python scikit-learn