K最邻近算法（下）

 1 import numpy as np
 2 import matplotlib.pyplot as plt
 3 from sklearn.datasets import make_blobs
 4 from sklearn.neighbors import KNeighborsRegressor
 5 from sklearn.datasets import  make_regression
 6 from sklearn.datasets import  load_wine
 7 from sklearn.model_selection import  train_test_split
 8 
 9 wine_dataset = load_wine()
10 X_train,X_test,y_train,y_test = train_test_split(wine_dataset[‘data‘],wine_dataset[‘target‘],random_state=0)
11 #将random_state = 0是因为tarin_test_split函数会生成一个为随机函数，并且会根据这个伪随机数对数据集进行拆分
12 knn = KNeighborsRegressor(n_neighbors=1)
13 
14 #查看参数设定
15 knn.fit(X_train,y_train)
16 print(knn)
17 print(‘模型得分：{:,.2f}‘.format(knn.score(X_test,y_test)))
18 
19 #预测新红酒的分类
20 X_new = np.array([[13.2, 2.77, 2.51, 18.5, 96.6, 1.04, 2.55, 0.57, 1.47, 6.21, 1.05, 3.33, 820]])
21 prediction = knn.predict(X_new)
22 print("预测新红酒的分类为:{}".format(wine_dataset[‘target_names‘][prediction]))
23 #print(‘X-_train shape:{}‘.format(X_train.shape))
24 # print("红酒数据集中的键:\n{}".format(wine_dataset.keys()))
25 #
26 # print("数据概况：{}".format(wine_dataset[‘data‘].shape))
27 #
28 # print(wine_dataset[‘DESCR‘])