如何针对具有13个特征的波士顿房价数据集（Boston Housing Dataset）应用随机森林和AdaBoost集成学习算法来预测房价中位数？请提供详细的步骤和可能的实现过程。

在Python中使用Scikit-Learn库来对波士顿房价数据集应用随机森林（Random Forest）和AdaBoost集成学习算法，可以按照以下步骤进行：

1. **导入所需库**:

   import pandas as pd
   from sklearn.datasets import load_boston
   from sklearn.model_selection import train_test_split
   from sklearn.ensemble import RandomForestRegressor, AdaBoostRegressor
   from sklearn.metrics import mean_squared_error, r2_score

2. **加载数据**:

   boston = load_boston()
   X = boston.data
   y = boston.target
   df = pd.DataFrame(boston.data, columns=boston.feature_names)

3. **数据预处理** (如果需要)：
- 缺失值检查
- 数据标准化或归一化

4. **拆分训练集和测试集**:

   X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

5. **初始化模型**:
- 随机森林：

     rf_model = RandomForestRegressor(n_estimators=100, random_state=42)

- AdaBoost Regressor：

     ada_model = AdaBoostRegressor(random_state=42)

6. **拟合模型**:

   rf_model.fit(X_train, y_train)
   ada_model.fit(X_train, y_train)

7. **预测**:

   rf_pred = rf_model.predict(X_test)
   ada_pred = ada_model.predict(X_test)

8. **评估模型性能**:

   rf_rmse = np.sqrt(mean_squared_error(y_test, rf_pred))
   rf_r2 = r2_score(y_test, rf_pred)

   ada_rmse = np.sqrt(mean_squared_error(y_test, ada_pred))
   ada_r2 = r2_score(y_test, ada_pred)

   print("Random Forest:")
   print(f"RMSE: {rf_rmse}, R^2 Score: {rf_r2}")
   print("AdaBoost Regressor:")
   print(f"RMSE: {ada_rmse}, R^2 Score: {ada_r2}")