C:\Users\wwwwww>python
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
Type "help", "copyright", "credits" or "license" for more information.
>>> 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)[/color]
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 "<stdin>", line 1, in <module>
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\externals\joblib\parallel.py", line 608, in dispatch_one_batch
self._dispatch(tasks)
File "E:\dsj-software\Anaconda3-4.4.0\lib\site-packages\sklearn\externals\joblib\parallel.py", line 571, in _dispatch
job = self._backend.apply_async(batch, callback=cb)
File "E:\dsj-software\Anaconda3-4.4.0\lib\site-packages\sklearn\externals\joblib\_parallel_backends.py", line 109, in apply_async
result = ImmediateResult(func)
File "E:\dsj-software\Anaconda3-4.4.0\lib\site-packages\sklearn\externals\joblib\_parallel_backends.py", line 326, in __init__
self.results = batch()
File "E:\dsj-software\Anaconda3-4.4.0\lib\site-packages\sklearn\externals\joblib\parallel.py", line 131, in __call__
return [func(*args, **kwargs) for func, args, kwargs in self.items]
File "E:\dsj-software\Anaconda3-4.4.0\lib\site-packages\sklearn\externals\joblib\parallel.py", line 131, in <listcomp>
return [func(*args, **kwargs) for func, args, kwargs in self.items]
File "E:\dsj-software\Anaconda3-4.4.0\lib\site-packages\sklearn\model_selection\_validation.py", line 260, in _fit_and_score
test_score = _score(estimator, X_test, y_test, scorer)
File "E:\dsj-software\Anaconda3-4.4.0\lib\site-packages\sklearn\model_selection\_validation.py", line 288, in _score
score = scorer(estimator, X_test, y_test)
File "E:\dsj-software\Anaconda3-4.4.0\lib\site-packages\sklearn\metrics\scorer.py", line 98, in __call__
**self._kwargs)
File "E:\dsj-software\Anaconda3-4.4.0\lib\site-packages\sklearn\metrics\classification.py", line 1239, in precision_score
sample_weight=sample_weight)
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.
>>>
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