sklearn의 cross_val_score()
Python에서의 모델 검증
Kasey Jones
Data Scientist
cross_val_score()
from sklearn.model_selection import cross_val_score
from sklearn.ensemble import RandomForestClassifier
rfc = RandomForestClassifier()
estimator: 사용할 모델
X: 예측 변수 데이터셋
y: 반응값 배열
cv: 교차 검증 분할 수
cross_val_score(estimator=rfc, X=X, y=y, cv=5)
scoring과 make_scorer 사용
cross_val_score의 scoring 매개변수:
# Load the Methods
from sklearn.metrics import mean_absolute_error, make_scorer
# Create a scorer
mae_scorer = make_scorer(mean_absolute_error)
# Use the scorer
cross_val_score(<estimator>, <X>, <y>, cv=5, scoring=mae_scorer)
모든 sklearn 메서드 불러오기
from sklearn.ensemble import RandomForestRegressor
from sklearn.model_selection import cross_val_score
from sklearn.metrics import mean_squared_error, make_scorer
모델과 스코어러 생성
rfc = RandomForestRegressor(n_estimators=20, max_depth=5, random_state=1111)
mse = make_scorer(mean_squared_error)
cross_val_score() 실행
cv_results = cross_val_score(rfc, X, y, cv=5, scoring=mse)
결과 확인
print(cv_results)
[196.765, 108.563, 85.963, 222.594, 140.942]
평균과 표준편차 출력:
print('The mean: {}'.format(cv_results.mean()))
print('The std: {}'.format(cv_results.std()))
The mean: 150.965
The std: 51.676
연습해 봅시다!
Python에서의 모델 검증
