Math & Numbers
使用梯度下降拟合二分类逻辑回归模型,并预测类别概率
用三种语言从你的代码中调用此工具。
curl -X POST 'https://api.elysiatools.com/zh/api/tools/logistic-regression-calculator' \
-H 'Content-Type: application/json' \
-d '{"csvData":"x1,x2,y\n0,1,0\n1,1,0\n1,2,0\n2,2,1\n3,2,1\n3,3,1\n4,3,1","hasHeaderRow":true,"predictionValues":"2.5, 2.5","learningRate":0.2,"iterations":3000,"threshold":0.5,"decimalPlaces":4}'以 JSON 形式 POST 提交输入参数。文件类型参数需先单独上传。
POST https://api.elysiatools.com/zh/api/tools/logistic-regression-calculator| 参数名 | 类型 | 必填 | 说明 |
|---|---|---|---|
| csvData | textarea | 否 | — |
| hasHeaderRow | checkbox | 否 | — |
| predictionValues | text | 否 | — |
| learningRate | number | 否 | — |
| iterations | number | 否 | — |
| threshold | number |
将此工具加入你的 Model Context Protocol 服务,让 AI 智能体可以列出并调用它。
将以下内容加入你的 MCP 客户端配置:
{
"mcpServers": {
"elysiatools-logistic-regression-calculator": {
"name": "logistic-regression-calculator",
"description": "使用梯度下降拟合二分类逻辑回归模型,并预测类别概率",
"baseUrl": "https://api.elysiatools.com/mcp/sse?toolId=logistic-regression-calculator",
"command": "",
"args": [],
"env": {},
"isActive": true,
"type": "sse"
}
}
}连接到 SSE 端点后,列出已开放的工具:
{
"jsonrpc": "2.0",
"id": 1,
"method": "tools/list"
}通过工具 id 调用,参数由其参数表构建:
{
"jsonrpc": "2.0",
"id": 2,
"method": "tools/call",
"params": {
"name": "logistic-regression-calculator",
"arguments": {
"csvData": "x1,x2,y\n0,1,0\n1,1,0\n1,2,0\n2,2,1\n3,2,1\n3,3,1\n4,3,1",
"hasHeaderRow": true,
"predictionValues": "2.5, 2.5",
"learningRate": 0.2,
"iterations": 3000,
"threshold": 0.5,
"decimalPlaces": 4
}
}
}有问题或反馈?请联系 [email protected]
| 否 |
| — |
| decimalPlaces | number | 否 | — |
JSON 结果
{
"key": {...},
"metadata": {
"key": "value"
},
"error": "Error message (optional)",
"message": "Notification message (optional)"
}