python中怎么按照KNN

在Python中，K近邻算法（K-Nearest Neighbors, KNN）通常通过`sklearn`库中的`KNeighborsClassifier`或`KNeighborsRegressor`来实现。以下是基本步骤：

1. **导入所需库**:

   from sklearn.neighbors import KNeighborsClassifier
   from sklearn.model_selection import train_test_split
   from sklearn.preprocessing import StandardScaler

2. **数据预处理**:
- 分割特征和目标变量
- 标准化数据（如果需要），因为KNN对输入值范围敏感：

   X = ...  # 特征数据
   y = ...  # 目标标签
   X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
   scaler = StandardScaler()
   X_train = scaler.fit_transform(X_train)
   X_test = scaler.transform(X_test)

3. **创建并训练模型**:

   k = 5  # 指定邻居数
   knn_model = KNeighborsClassifier(n_neighbors=k)
   knn_model.fit(X_train, y_train)

4. **预测**:

   predictions = knn_model.predict(X_test)

5. **评估模型性能**:

   from sklearn.metrics import accuracy_score, classification_report
   print("Accuracy:", accuracy_score(y_test, predictions))
   print(classification_report(y_test, predictions))