scikit-learn初步，一个KNN算法示例

import numpy as np
from sklearn import datasets #数据集
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
from sklearn.neighbors import KNeighborsClassifier #引人KNN算法
iris = datasets.load_iris() #从datasets里载入iris的数据
iris_X = iris.data
iris_y = iris.target
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
knn = KNeighborsClassifier() 
knn.fit(X_train,y_train) #训练

用训练好的knn做预测

print(knn.predict(X_test)) #打印预测结果
print(y_test) #打印真实结果
[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]
[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]