code_2.py

from sklearn.neighbors import KNeighbors Classifier # 导入KNN模型
K = 5 # 设定初始K值为5
KNN = KNeighbors Classifier(n_neighbors = K)　# KNN模型
KNN.fit(X_train, y_train) # 拟合KNN模型
y_pred = KNN.predict(X_test) # 预测心脏病结果
from sklearn.metrics import (f1_score, confusion_matrix) # 导入评估指标
print("{}NN预测准确率: {:.2f}%".format(K, KNN.score(X_test, y_test)*100))
print("{}NN预测F1分数: {:.2f}%".format(K, f1_score(y_test, y_pred)*100))
print('KNN混淆矩阵:\n', confusion_matrix(y_pred, y_test))