写python中利用KNN进行分类实验，通过调整参数K，对算法性能指标包括准确率、精确率、召回率、F1分数进行比较的代码

以下是利用KNN进行分类实验并比较性能指标的Python代码示例：

from sklearn.neighbors import KNeighborsClassifier
from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score
from sklearn.model_selection import train_test_split

# 加载数据集
# X为特征数据，y为目标数据
X, y = load_dataset()
# 划分训练集和测试集
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# 初始化KNN分类器
knn = KNeighborsClassifier()

# 调整参数K的范围
k_values = [3, 5, 7, 9]

# 初始化性能指标列表
accuracy_scores = []
precision_scores = []
recall_scores = []
f1_scores = []

# 对每个K值进行实验并计算性能指标
for k in k_values:
    # 设置KNN的K值
    knn.n_neighbors = k
    
    # 拟合训练数据
    knn.fit(X_train, y_train)
    
    # 预测测试数据
    y_pred = knn.predict(X_test)
    
    # 计算准确率、精确率、召回率和F1分数
    accuracy = accuracy_score(y_test, y_pred)
    precision = precision_score(y_test, y_pred)
    recall = recall_score(y_test, y_pred)
    f1 = f1_score(y_test, y_pred)
    
    # 将结果添加到性能指标列表中
    accuracy_scores.append(accuracy)
    precision_scores.append(precision)
    recall_scores.append(recall)
    f1_scores.append(f1)

# 打印各个K值对应的性能指标结果
for i, k in enumerate(k_values):
    print(f"For K = {k}:")
    print(f"Accuracy: {accuracy_scores[i]}")
    print(f"Precision: {precision_scores[i]}")
    print(f"Recall: {recall_scores[i]}")
    print(f"F1 Score: {f1_scores[i]}")
    print()