패키지는 동일 : from sklearn.metrics import ...

• confusion_matrix(y_true, y_pred)

• accuracy_score(y_true, y_pred)

• precision_score(y_true, y_pred)

• recall_score(y_true, y_pred)

• fbeta_score(y_true, y_pred, beta)

• f1_score(y_true, y_pred)

• classfication_report(y_true, y_pred)

• roc_curve

• auc

confusion_matrix(y_true, y_pred) : 분류 결과표

분류결과표는 타켓의 원래 클래스와 모형이 예측한 크래스가 일치하는지는 갯수로 센 결과를 표로 나타낸 것

- 사용법

분류결과표 출력

from sklearn.metrics import confusion_matrix
y_true = [2, 0, 2, 2, 0, 1]
y_pred = [0, 0, 2, 2, 0, 2]

confusion_matrix(y_true, y_pred)

classfication_report(y_true, y_pred) : 분류 보고서

- 사용법

from sklearn.metrics import classification_report

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

print(classification_report(y,y_pred,target_names=['class 0','class 1']))

accuracy_score(y_true, y_pred) : 정확도 계산

- 사용법

from sklearn.metrics import accuracy_score

accuracy_score(y1, y1_pred)