python中K-NN算法是什么？

from sklearn.datasets import load_iris

from sklearn.model_selection import train_test_split

from sklearn.preprocessing import StandardScaler

from sklearn.neighbors import KNeighborsClassifier

# 1.获取数据

iris = load_iris()

# 2.数据基本处理：训练集的特征值x_train 测试集的特征值x_test 训练集的⽬标值y_train 测试集的⽬标值y_test，

'''

x: 数据集的特征值

y: 数据集的标签值

test_size: 测试集的⼤⼩， ⼀般为float

random_state: 随机数种⼦,不同的种⼦会造成不同的随机采样结果。 相同的种⼦采样结果相同

'''

x_train, x_test, y_train, y_test = train_test_split(iris.data, iris.target, test_size=0.2, random_state=22)

# 3.特征工程 - 特征预处理

transfer = StandardScaler()

x_train = transfer.fit_transform(x_train)

x_test = transfer.transform(x_test)

# 4.机器学习-KNN

# 4.1 实例化一个估计器

estimator = KNeighborsClassifier(n_neighbors=5)

# 4.2 模型训练

estimator.fit(x_train, y_train)

# 5.模型评估

# 5.1 预测值结果输出

y_pre = estimator.predict(x_test)

print("预测值是:\n", y_pre)

print("预测值和真实值的对比是:\n", y_pre==y_test)

# 5.2 准确率计算

score = estimator.score(x_test, y_test)

print("准确率为:\n", score)