In [7]:

# 실제 관측 값 출력
print('Actual value: ', y_test.values)

Actual value: [1 0]

In [8]:

# 테스트 데이터셋에서 모델을 평가합니다
from sklearn.metrics import classification_report
from sklearn.metrics import confusion_matrix, accuracy_score
pred = classifier.predict(X_test)
print(classification_report(y_test,pred))
print('Confusion Matrix: \n', confusion_matrix(y_test,pred))
print()
print('Accuracy: ', accuracy_score(y_test,pred))

              precision    recall    f1-score    support

           0       0.00      0.00        0.00          1
           1       0.50      1.00        0.67          1

    accuracy                             0.50          2
   macro avg       0.25      0.50        0.33          2
weighted avg       0.25      0.50        0.33          2

Confusion Matrix:
[[0 1]
 [0 1]]

Accuracy: 0.5