数据挖掘｜cross_val_score 交叉验证使用

from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.model_selection import cross_val_score

# 鸢尾花数据集
iris = load_iris()
X = iris.data
y = iris.target

# K近邻和逻辑回归对比，交叉验证取10折
from sklearn.neighbors import KNeighborsClassifier
knn = KNeighborsClassifier(n_neighbors=20)
print(cross_val_score(knn, X, y, cv=10, scoring='accuracy').mean())

# 交叉验证同样取10折
from sklearn.linear_model import LogisticRegression
logreg = LogisticRegression()
print(cross_val_score(logreg, X, y, cv=10, scoring='accuracy').mean())
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0.9800000000000001
0.9733333333333334
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