1 import numpy as np
 2 from sklearn import datasets #數據集
 3 from sklearn.model_selection import train_test_split #train_test_split用來把數據分為訓練集和測試集
 4 from sklearn.neighbors import KNeighborsClassifier #引人KNN算法
 5 iris = datasets.load_iris() #從datasets裏載入iris的數據
 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) #分割訓練集和測試集
 9 knn = KNeighborsClassifier() 
10 knn.fit(X_train,y_train) #訓練

用訓練好的knn做預測

1 print(knn.predict(X_test)) #打印預測結果
2 print(y_test) #打印真實結果
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]

scikit-learn初步，一個KNN算法示例