如下所示：
|
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
|
from sklearn.datasets import load_iris iris = load_iris() print iris.data.shape from sklearn.cross_validation import train_test_split X_train, X_test, y_train, y_test = train_test_split(iris.data, iris.target, test_size = 0.25, random_state = 33) from sklearn.preprocessing import StandardScalerfrom sklearn.neighbors import KNeighborsClassifier ss = StandardScaler() X_train = ss.fit_transform(X_train)X_test = ss.transform(X_test) knc = KNeighborsClassifier()knc.fit(X_train, y_train)y_predict = knc.predict(X_test) print 'The accuracy of K-Nearest Neighbor Classifier is: ', knc.score(X_test, y_test) from sklearn.metrics import classification_report print classification_report(y_test, y_predict, target_names = iris.target_names) |
以上这篇在python中利用KNN实现对iris进行分类的方法就是小编分享给大家的全部内容了，希望能给大家一个参考，也希望大家多多支持服务器之家。
原文链接：https://blog.csdn.net/UESTC_C2_403/article/details/72848826
