47 lines
1.5 KiB
Python
47 lines
1.5 KiB
Python
# 导入必要的库
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from sklearn.datasets import fetch_california_housing
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from sklearn.model_selection import train_test_split
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from sklearn.preprocessing import StandardScaler
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from sklearn.neighbors import KNeighborsRegressor
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from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score
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import matplotlib.pyplot as plt
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# 1. 加载数据集（使用加州房价数据集）
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data = fetch_california_housing(data_home="/home/admin-root/haotian/ML/dataset")
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X = data.data # 特征数据
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y = data.target # 目标数据（房价）
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# 2. 划分数据集（80%训练，20%测试）
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X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
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# 3. 特征标准化（KNN对特征尺度敏感）
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scaler = StandardScaler()
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X_train = scaler.fit_transform(X_train)
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X_test = scaler.transform(X_test)
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# 4. 构建KNN回归模型（这里K=5）
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knn = KNeighborsRegressor(n_neighbors=5)
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# 5. 训练模型
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knn.fit(X_train, y_train)
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# 6. 预测
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y_pred = knn.predict(X_test)
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# 7. 评估模型性能
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mae = mean_absolute_error(y_test, y_pred)
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mse = mean_squared_error(y_test, y_pred)
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r2 = r2_score(y_test, y_pred)
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# 打印评估指标
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print(f"Mean Absolute Error: {mae:.4f}")
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print(f"Mean Squared Error: {mse:.4f}")
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print(f"R^2 Score: {r2:.4f}")
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# 可视化预测结果 vs 实际值（仅在二维数据时更直观）
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plt.scatter(y_test, y_pred, c='blue', edgecolors='k', alpha=0.7)
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plt.xlabel("True Values")
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plt.ylabel("Predictions")
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plt.title("KNN Regression - True vs Predicted")
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plt.savefig("./output/knn_r.png")
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