Cross Validation

cross_val_score

from sklearn.model_selection import cross_val_score

scores = cross_val_score(model, X_train, y_train, scoring='accuracy') #default cv=5

scores = cross_val_score(model, X_train, y_train, cv=30)

LOOCV(Leave-One-Out Cross-Validation)

• 데이터가 매우 적을때 사용

# LOOCV is very time-consuming => useful in small data

from sklearn.model_selection import LeaveOneOut

scores = cross_val_score(model, X_train, y_train, cv=LeaveOneOut())
scores.mean()

Shuffle-Split Cross-Validation

from sklearn.model_selection import ShuffleSplit

sscv = ShuffleSplit(test_size=.5, train_size=.4, n_splits=10)
scores = cross_val_score(model, X_train, y_train, cv=sscv)
scores.mean()
scores