{
  "schemaVersion": 1,
  "id": "2.2.4",
  "title": "低碳生活行为影响因素预测线性回归模型开发与测试",
  "durationMinutes": 20,
  "category": "模型开发与测试",
  "scenario": "在应对气候变化的背景下，了解和促进低碳生活行为变得越来越重要。现要求根据提供的“大学生低碳生活行为的影响因素数据集”，补全2.2.4.ipynb代码。选择合适的特征，开发一个预测大学生低碳生活行为的模型。利用测试工具对模型进行测试，并对测试结果进行分析，完成测试报告，并运用工具对错误原因进行纠正。\n\n（1）正确加载数据集，并显示前五行的数据。\n\n（2）请使用线性回归模型，要求设定自变量和因变量，并根据自变量特征进行模型训练，最终将训练好的模型以文件名2.2.4_model.pkl保存到考生文件夹，结果文件以2.2.4_results.txt保存到考生文件夹。\n\n（3）使用测试工具对模型进行测试，并记录测试结果，命名2.2.4_report.txt，保存到考生文件夹。\n\n（4）对测试结果进行详细分析，并编写测试报告，包括模型性能评估、错误分析及改进建议，将答案写到答题卷文件中，答题卷文件命名为“2.2.4.docx”，保存到考生文件夹。\n\n（5）运用工具分析算法中错误案例产生的原因并进行纠正，重新得到模型训练结果，以文件名2.2.4_results_xg.txt保存到考生文件夹。\n\n（6）将以上代码以及运行结果，以html格式保存并命名为2.2.4.html，保存到考生文件夹，考生文件夹命名为“准考证号+身份证后6位”。",
  "skillRequirements": "(1) 能够维护日常训练集与测试集。\n\n(2) 能使用工具对算法进行训练。\n\n(3) 能够使用测试工具对人工智能产品的使用进行测试。\n\n(4) 能够对测试结果进行分析，编写测试报告。\n\n(5) 能够运用工具，分析算法中错误案例产生的原因并进行纠正。",
  "qualityIndicators": "(1) 深入理解业务，训练符合业务需求的模型。\n\n(2) 数据预处理步骤完整，方法选择合理。\n\n(3) 代码实现正确，结果符合预期。\n\n(4) 测试结果分析全面，报告详细。",
  "tasks": [
    {
      "id": "code-fill",
      "type": "code-fill",
      "title": "补全代码任务",
      "instructions": "依据公开题面和附件补全代码空位；仅检查完成度与提交格式，不公开标准内容。",
      "codeBlocks": [
        "import pandas as pd\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.linear_model import LinearRegression\nfrom sklearn.metrics import mean_squared_error, r2_score\nimport joblib\nfrom xgboost import XGBRegressor\n\n# 加载数据集\ndata = __________\n\n# 显示数据集的前五行\nprint(__________)\n\n# 删除不必要的列并处理分类变量\ndata_cleaned = __________(__________=['序号', '所用时间'])  # 删除不必要的列\ndata_cleaned = pd.get_dummies(data_cleaned, drop_first=True)  # 将分类变量转换为哑变量/指示变量\n\n# 定义目标变量和特征\ntarget = '5.您进行过绿色低碳的相关生活方式吗?'  # 确保这是目标变量\n\n# 定义自变量和因变量\nX = __________(__________=__________)\ny = __________\n\n# 将数据拆分为训练集和测试集（测试集占20%）\nX_train, X_test, y_train, y_test = __________(__________, random_state=42)\n\n# 初始化线性回归模型\nmodel = __________\n# 训练线性回归模型\n__________\n\n# 保存训练好的模型\nmodel_filename = '2.2.4_model.pkl'\njoblib.__________\n\n# 进行预测\ny_pred = __________\n\n# 将结果保存到文本文件中\nresults = pd.DataFrame({'实际值': y_test, '预测值': y_pred})\nresults_filename = '2.2.4_results.txt'\n__________(__________, index=False, sep='\\t')  # 使用制表符分隔值保存到文本文件\n\n# 将测试结果保存到报告文件中\nreport_filename = '2.2.4_report.txt'\nwith open(report_filename, 'w') as f:\n    f.write(f'均方误差: {__________}\\n')\n    f.write(f'决定系数: {__________}\\n')\n    \n# 分析并纠正错误（示例：使用XGBoost）\n# 初始化XGBoost模型（设定树的数量为1000，学习率为0.05，每棵树的最大深度为5，）\nxgb_model = __________(__________, subsample=0.8, colsample_bytree=0.8)\n# 训练XGBoost模型\n__________\n\n# 使用XGBoost模型进行预测\ny_pred_xg = __________\n\n# 将XGBoost结果保存到文本文件中\nresults_xg_filename = '2.2.4_results_xg.txt'\nresults_xg = pd.DataFrame({'实际值': y_test, '预测值': y_pred_xg})\nresults_xg.to_csv(results_xg_filename, index=False, sep='\\t')  # 使用制表符分隔值保存到文本文件\n\n# 将XGBoost测试结果保存到报告文件中\nreport_filename_xgb = '2.2.4_report_xgb.txt'\nwith open(report_filename_xgb, 'w') as f:\n    f.write(f'均方误差: {__________}\\n')\n    f.write(f'决定系数: {__________}\\n')\n",
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      "type": "artifact-submission",
      "title": "选择结果文件",
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