完成--随机森林回归示例

dataset/CaliforniaHousing/cal_housing.domain

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+longitude: continuous.
+latitude: continuous.
+housingMedianAge: continuous. 
+totalRooms: continuous. 
+totalBedrooms: continuous. 
+population: continuous. 
+households: continuous. 
+medianIncome: continuous. 
+medianHouseValue: continuous. 

random_forest_regression.py

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+# 导入必要的库
+from sklearn.ensemble import RandomForestRegressor
+# from sklearn.datasets import fetch_california_housing
+from sklearn.model_selection import train_test_split
+from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score
+import numpy as np
+import matplotlib.pyplot as plt
+
+import numpy as np
+import pandas as pd
+
+# 指定本地数据路径
+data_path = "/home/admin-root/haotian/ML/dataset/CaliforniaHousing"
+
+# 加载数据文件
+# X = np.loadtxt(f"{data_path}/cal_housing.data", delimiter=" ")
+X = np.genfromtxt(f"{data_path}/cal_housing.data", delimiter=',', dtype=float)
+
+# 读取特征名称 (如果 domain 文件是特征的描述)
+with open(f"{data_path}/cal_housing.domain", "r") as f:
+    feature_names = [line.strip() for line in f.readlines()]
+
+# 显示特征名称，检查是否正确
+print("Feature Names:", feature_names)
+
+# 假设目标变量是 `X` 的最后一列，按列拆分
+y = X[:, -1]  # 目标变量（房价）
+X = X[:, :-1]  # 特征数据
+
+# # 1. 加载数据集（使用加州房价数据集）
+# data = fetch_california_housing(data_home="/home/admin-root/haotian/ML/dataset/CaliforniaHousing")
+# X = data.data  # 特征
+# y = data.target  # 目标变量（房价）
+
+# 2. 划分训练集和测试集（80% 训练，20% 测试）
+X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
+
+# 3. 创建随机森林回归模型
+rf_regressor = RandomForestRegressor(n_estimators=100, random_state=42)
+
+# 4. 训练模型
+rf_regressor.fit(X_train, y_train)
+
+# 5. 进行预测
+y_pred = rf_regressor.predict(X_test)
+
+# 6. 评估模型
+mae = mean_absolute_error(y_test, y_pred)
+mse = mean_squared_error(y_test, y_pred)
+rmse = np.sqrt(mse)
+r2 = r2_score(y_test, y_pred)
+
+print(f"均绝对误差（MAE）：{mae:.4f}")
+print(f"均方误差（MSE）：{mse:.4f}")
+print(f"均方根误差（RMSE）：{rmse:.4f}")
+print(f"R² 评分（R2 Score）：{r2:.4f}")
+
+# 7. 可视化预测结果
+plt.figure(figsize=(8, 6))
+plt.scatter(y_test, y_pred, alpha=0.6)
+plt.plot([min(y_test), max(y_test)], [min(y_test), max(y_test)], '--r')  # 理想预测线
+plt.xlabel("true_label")
+plt.ylabel("predict_label")
+plt.title("random_forest_regression")
+plt.savefig('./output/random_forest_r.png')
+
+# # 8. 特征重要性可视化
+# feature_importances = rf_regressor.feature_importances_
+# plt.figure(figsize=(10, 6))
+# plt.barh(feature_names, feature_importances)
+# plt.xlabel("feature importance")
+# plt.ylabel("feature name")
+# plt.title("random_forest_regression - feature_importance")
+# plt.savefig('./output/random_forest_r_feature.png')