Skip to main content
Glama

interpret_model_fit

Interpret statistical model fit metrics like R², AIC, BIC, and Pseudo-R² to evaluate model performance and compare models for quantitative research.

Instructions

모형적합도 해석 (R², AIC, BIC, Pseudo-R²)

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
metricsYes적합도 지표들
model_typeYes모형 유형
comparisonNo모형 비교 여부

Implementation Reference

  • The handler function for 'interpret_model_fit' tool. Takes metrics object and returns interpretations for R², adjusted R², AIC, BIC in Korean.
    function handleInterpretModelFit(args: Record<string, unknown>) { const metrics = args.metrics as Record<string, number>; return { metrics, interpretation: { r_squared: metrics.r_squared ? `설명력: ${(metrics.r_squared * 100).toFixed(1)}%` : null, adj_r_squared: metrics.adj_r_squared ? `조정된 설명력: ${(metrics.adj_r_squared * 100).toFixed(1)}%` : null, aic: metrics.aic ? "낮을수록 좋음 (모형 비교용)" : null, bic: metrics.bic ? "낮을수록 좋음 (복잡성 페널티 더 큼)" : null } }; }
  • Input schema for the 'interpret_model_fit' tool defining required metrics object, model_type string, and optional comparison boolean.
    inputSchema: { type: "object", properties: { metrics: { type: "object", description: "적합도 지표들" }, model_type: { type: "string", description: "모형 유형" }, comparison: { type: "boolean", description: "모형 비교 여부" }, }, required: ["metrics", "model_type"], },
  • Tool registration in the tools array, including name, description, and inputSchema.
    name: "interpret_model_fit", description: "모형적합도 해석 (R², AIC, BIC, Pseudo-R²)", inputSchema: { type: "object", properties: { metrics: { type: "object", description: "적합도 지표들" }, model_type: { type: "string", description: "모형 유형" }, comparison: { type: "boolean", description: "모형 비교 여부" }, }, required: ["metrics", "model_type"], }, },
  • Registration of the handler in the main handleToolCall switch statement.
    case "interpret_model_fit": return handleInterpretModelFit(args);

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/seanshin0214/quantmaster-mcp-server'

If you have feedback or need assistance with the MCP directory API, please join our Discord server