#k近邻法算法原理，根据训练集数据，对于新数据进行数据的定位，n_neighbors参数=[1,3,6,9.....],参数越小则模型的复杂度越高。参数越多则决策边界越平滑。（基于python机器学习基础教程）

from sklearn.neighbors import KNeighborsClassifier

from sklearn.model_selection import train_test_split

import pandas as pd

import numpy as np

import matplotlib.pyplot as plt

import mglearn 

x,y=mglearn.datasets.make_forge()

x_train,x_test,y_train,y_test=train_test_split(x,y,random_state=0)

knn3=KNeighborsClassifier(n_neighbors=3)

knn3.fit(x_train,y_train)

y_pred=knn3.predict(x_test)

print(knn3.score(x_test,y_test))