KNN回归：通过折叠交叉验证来寻找最佳的k值并绘制折线图

from sklearn import datasets
from sklearn.neighbors import KNeighborsClassifier
from sklearn.model_selection import GridSearchCV

# 导入鸢尾花数据集
iris = datasets.load_iris()
X = iris.data
y = iris.target

# 设置需要搜索的K值
parameters = {'n_neighbors': [1, 3, 5, 7, 9, 11, 13, 15]}

# 创建KNN分类器
knn = KNeighborsClassifier()

# 使用GridSearchCV来找到最佳的K值
clf = GridSearchCV(knn, parameters, cv=5)
clf.fit(X, y)

# 输出最佳参数及相应的准确度
print("最佳准确度：%.2f" % clf.best_score_, "最佳K值:", clf.best_params_)

import matplotlib.pyplot as plt

# 提取每个K值对应的准确度和K值
k_values = [param['n_neighbors'] for param in clf.cv_results_['params']]
accuracies = clf.cv_results_['mean_test_score']

# 绘制折线图
plt.plot(k_values, accuracies, marker='o')
plt.title('Grid Search Results')
plt.xlabel('K Value')
plt.ylabel('Accuracy')
plt.show()

参考：回归模型 第5篇：knn回归-CSDN博客