Python机器学习实验二：编写代码，实现对iris数据集的KNN算法分类及预测
1、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。
import numpy as npfrom sklearn.model_selection import train_test_splitfrom sklearn.datasets import load_irisfrom sklearn.neighbors import KNeighborsClassifier #导入K紧邻分类算法from sklearn.metrics import precision_scoreimport matplotlib.pyplot as plt#导入iris数据集iris = load_iris()x = iris.data# print(x)y = iris.target#划分数据集和测试集x_train,x_test,y_train,y_test = train_test_split(x,y,test_size=0.2)#用KNN模型进行训练和预测knn = KNeighborsClassifier(n_neighbors=5)knn = knn.fit(x_train,y_train)y_pre = knn.predict(x_test)#对预测结果进行评价s = precision_score(y_test,y_pre,average=None)#此处average不赋值的话会报错print("模型的准确率为：",'\n',s)#预测未知模型outputLabel = knn.predict([[1.5,3,5.8,2.2],[6.2,2.9,4.3,1.3]])print("预测出未知数据的标签为：",outputLabel)# cValue = ['r','b']*75# plt.scatter(x[:,0],x[:,1],c = cValue,marker='o')fig = plt.figure()ax = fig.add_subplot(111)t = ax.scatter(x_train[:,1],x_train[:,0],10.0 * np.array(y_train),np.array(y_train))plt.show()
点个👍吧
点个👍吧
