Z-Score标准化工具

POST https://api.elysiatools.com/zh/api/tools/data-zscore-normalizer

{
  "mcpServers": {
    "elysiatools-data-zscore-normalizer": {
      "name": "data-zscore-normalizer",
      "description": "使用Z-Score（标准分数）标准化数值数据，使数据均值为0，标准差为1。完美用于统计分析、机器学习特征预处理、异常检测和不同尺度数据比较。\n\n功能特点：\n- Z-Score标准化（均值=0，标准差=1）\n- 稳健Z-Score选项（使用中位数和MAD）\n- 自定义缩放到目标范围\n- 多列选择\n- 自动数据类型检测\n- 智能处理缺失值\n- 保留非数值列\n- 综合统计摘要\n- 异常值检测和报告\n\n常见用途：\n- 机器学习特征准备\n- 统计假设检验\n- 异常值检测和移除\n- 不同单位数据比较\n- 主成分分析（PCA）预处理",
      "baseUrl": "https://api.elysiatools.com/mcp/sse?toolId=data-zscore-normalizer",
      "command": "",
      "args": [],
      "env": {},
      "isActive": true,
      "type": "sse"
    }
  }
}

{
  "jsonrpc": "2.0",
  "id": 1,
  "method": "tools/list"
}

{
  "jsonrpc": "2.0",
  "id": 2,
  "method": "tools/call",
  "params": {
    "name": "data-zscore-normalizer",
    "arguments": {
  "inputData": "name,age,salary,score\nAlice,25,50000,85.5\nBob,30,75000,92.3\nCharlie,35,120000,78.9",
  "targetColumns": "age, salary 或 name, email\n\n留空则自动检测数值列",
  "standardizationType": "zscore",
  "outputRange": "0, 1",
  "handleMissing": "skip",
  "preserveOriginal": false,
  "decimalPlaces": 4,
  "includeStatistics": true,
  "detectOutliers": true,
  "outlierThreshold": 2
}
  }
}