使用sklearn获得相同的精度和召回（K-NN）值

Question

更新的问题： 我这样做，但我得到了同样的结果，既精度和召回是因为我使用average ='binary'？

但是当我使用平均= '宏观' 我收到此错误信息：

测试定制审核 messageC：\ Python27 \ LIB \站点包\ sklearn \指标\ classification.py：976 ： DeprecationWarning：从版本0.18开始，当使用平均精确度/召回率/ F-分数时，二进制输入将不是 。请 使用average ='binary'仅报告正面课堂表现。
“正级性能”，DeprecationWarning）

这是我更新的代码：

path = 'opinions.tsv' 
data = pd.read_table(path,header=None,skiprows=1,names=['Sentiment','Review']) 
X = data.Review 
y = data.Sentiment 
#Using CountVectorizer to convert text into tokens/features 
vect = CountVectorizer(stop_words='english', ngram_range = (1,1), max_df = .80, min_df = 4) 
X_train, X_test, y_train, y_test = train_test_split(X,y,random_state=1, test_size= 0.2) 
#Using training data to transform text into counts of features for each message 
vect.fit(X_train) 
X_train_dtm = vect.transform(X_train) 
X_test_dtm = vect.transform(X_test) 




#Accuracy using KNN Model 
KNN = KNeighborsClassifier(n_neighbors = 3) 
KNN.fit(X_train_dtm, y_train) 
y_pred = KNN.predict(X_test_dtm) 
print('\nK Nearest Neighbors (NN = 3)') 



#Naive Bayes Analysis 
tokens_words = vect.get_feature_names() 
print '\nAnalysis' 
print'Accuracy Score: %f %%'% (metrics.accuracy_score(y_test,y_pred)*100) 
print "Precision Score: %f%%" % precision_score(y_test,y_pred, average='binary') 
print "Recall Score: %f%%" % recall_score(y_test,y_pred, average='binary') 

通过使用上面的代码，我得到相同价值的准确率和召回

谢谢你回答我的问题，非常感谢。

Answer 1

要计算precision和recall指标，你应该从sklearn.metrics导入根据的方法。

如文档中表示，它们的参数是真正的1-d阵列和预测标签：

from sklearn.metrics import precision_score 
from sklearn.metrics import recall_score 

y_true = [0, 1, 2, 0, 1, 2] 
y_pred = [0, 2, 1, 0, 0, 1] 

print('Calculating the metrics...') 

recision_score(y_true, y_pred, average='macro') 
>>> 0.22 

recall_score(y_true, y_pred, average='macro') 
>>> 0.33