使用sklearn獲得相同的精度和召回（K-NN）值

Question

更新的問題： 我這樣做，但我得到了同樣的結果，既精度和召回是因爲我使用average ='binary'？

但是當我使用平均= '宏觀' 我收到此錯誤信息：

測試定製審覈 messageC：\ Python27 \ LIB \站點包\ sklearn \指標\ classification.py：976 ： DeprecationWarning：從版本0.18開始，當使用平均精確度/召回率/ F-分數時，二進制輸入將不是 。請 使用average ='binary'僅報告正面課堂表現。
「正級性能」，DeprecationWarning）

這是我更新的代碼：

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') 

通過使用上面的代碼，我得到相同價值的準確率和召回

謝謝你回答我的問題，非常感謝。

Answer 1

要計算precision和recall指標，你應該從sklearn.metrics導入根據的方法。

如文檔中表示，它們的參數是真正的1-d陣列和預測標籤：

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