1 import numpy as np
2 from sklearn
import datasets
#数据集
3 from sklearn.model_selection
import train_test_split
#train_test_split用来把数据分为训练集和测试集
4 from sklearn.neighbors
import KNeighborsClassifier
#引人KNN算法
5 iris = datasets.load_iris()
#从datasets里载入iris的数据
6 iris_X =
iris.data
7 iris_y =
iris.target
8 X_train,X_test,y_train,y_test = train_test_split(iris_X,iris_y,test_size=0.3)
#分割训练集和测试集
9 knn =
KNeighborsClassifier()
10 knn.fit(X_train,y_train)
#训练
用训练好的knn做预测
1 print(knn.predict(X_test))
#打印预测结果
2 print(y_test)
#打印真实结果
3 [1 1 0 0 2 0 2 1 0 1 0 2 2 0 2 2 1 2 1 0 1 1 1 0 2 1 1 0 0 1 1 0 1 1 1 0 2 1 2 0 2 0 1 1 1
]
4 [1 1 0 0 2 0 2 1 0 1 0 2 2 0 2 2 2 2 1 0 1 1 1 0 2 1 1 0 0 1 1 0 1 1 1 0 2 1 2 0 1 0 1 1 1]
转载于:https://www.cnblogs.com/imageSet/p/7624414.html
