corss_val_score的scoring='accuracy'可以执行，使用其它参数报错

Python 3.6.1 |Anaconda 4.4.0 (64-bit)| (default, May 11 2017, 13:25:24) [MSC v.1900 64 bit (AMD64)] on win32

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>>> from sklearn.model_selection import cross_val_score

>>> from sklearn.datasets import load_iris

>>> from sklearn.ensemble import RandomForestClassifier

>>> forest = RandomForestClassifier(n_estimators=100,random_state=1)

>>> cross_val_score(forest,load_iris().data,load_iris().target,scoring='accuracy',cv=5)

array([ 0.96666667, 0.96666667, 0.93333333, 0.96666667, 1. ])

>>> cross_val_score(forest,load_iris().data,load_iris().target,scoring='precision',cv=5)

Traceback (most recent call last):

  File "", line 1, in

  File "E:\dsj-software\Anaconda3-4.4.0\lib\site-packages\sklearn\model_selection\_validation.py", line 140, in cross_val_score

    for train, test in cv_iter)

  File "E:\dsj-software\Anaconda3-4.4.0\lib\site-packages\sklearn\externals\joblib\parallel.py", line 758, in __call__

    while self.dispatch_one_batch(iterator):

  File "E:\dsj-software\Anaconda3-4.4.0\lib\site-packages\sklearn\metrics\classification.py", line 1018, in precision_recall_fscore_support

    "choose another average setting." % y_type)

ValueError: Target is multiclass but average='binary'. Please choose another average setting.

>>>