用外部库实现knn分类算法，并计算正确率

from sklearn.model_selection import train_test_split
from sklearn.datasets import load_iris
from sklearn.neighbors import KNeighborsClassifier
import numpy as np
cheng = load_iris()
# print(cheng.keys())
# print(cheng.data)
# print(cheng.target)
# print(cheng.target_names)
# print(cheng.DESCR)
# print(cheng.feature_names)
# print(cheng.filename)
trainX, testX, trainY, tesY = train_test_split(
    cheng.data, cheng.target, random_state=0)
knn = KNeighborsClassifier(n_neighbors=5)
knn.fit(trainX, trainY)
testy = knn.predict(testX)
zhv = np.mean(testy == tesY)
print(zhv)

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原文地址：https://www.cnblogs.com/lovelyemperor/p/10801273.html