2. 编写代码，实现对iris数据集的KNN算法分类及预测

要求：

（1）数据集划分为测试集占20%；
（2）n_neighbors=5；
（3）评价模型的准确率；
（4）使用模型预测未知种类的鸢尾花。
（待预测数据：X1=[[1.5 , 3 , 5.8 , 2.2], [6.2 , 2.9 , 4.3 , 1.3]]）

iris数据集有150组，每组4个数据。

第一步：引入所需库

from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier
import numpy as np

第二步：划分测试集占20%

 x_train, x_test, y_train, y_test = train_test_split(data, target, test_size=0.2, random_state=0)

test_size为0-1的数代表占百分之几
random_state为零随机数确定，每次结果都相同

第三步：n_neighbors=5

 KNeighborsClassifier(n_neighbors=5)

第四步：评价模型的准确率

KNN.fit(x_train, y_train)
# 训练集准确率
train_score = KNN.score(x_train, y_train)
# 测试集准确率
test_score = KNN.score(x_test, y_test)

第五步：使用模型预测未知种类的鸢尾花

#待预测数据：X1=[[1.5 , 3 , 5.8 , 2.2], [6.2 , 2.9 , 4.3 , 1.3]]
 X1 = np.array([[1.5, 3, 5.8, 2.2], [6.2, 2.9, 4.3, 1.3]])
 # 进行预测
 prediction = KNN.predict(X1)
 # 种类名称
 k = iris.get("target_names")[prediction]

完整代码：

from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier
import numpy as np
if __name__ == '__main__':
    iris = load_iris()
    data = iris.get("data")
    target = iris.get("target")
    x_train, x_test, y_train, y_test = train_test_split(data, target, test_size=0.2, random_state=0)
    KNN = KNeighborsClassifier(n_neighbors=5)
    KNN.fit(x_train, y_train)
    train_score = KNN.score(x_train, y_train)
    test_score = KNN.score(x_test, y_test)
    print("模型的准确率：", test_score)
    X1 = np.array([[1.5, 3, 5.8, 2.2], [6.2, 2.9, 4.3, 1.3]])
    prediction = KNN.predict(X1)
    k = iris.get("target_names")[prediction]
    print("第一朵花的种类为：", k[0])
    print("第二朵花的种类为：", k[1])

结果: