Analyze ML experiment results, compute statistics, generate comparison tables and insights. Use when user says "analyze results", "compare", or needs to interpret experimental data.

Analyze Experiment Results Analyze: $ARGUMENTS Workflow Step 1: Locate Results Find all relevant JSON/CSV result files: Check figures/ , results/ , or project specific output directories Parse JSON results into structured data Step 2: Build Comparison Table Organize results by: Independent variables : model type, hyperparameters, data config Dependent variables : primary metric (e.g., perplexity, accuracy, loss), secondary metrics Delta vs baseline : always compute relative improvement Step 3: Statistical Analysis If multiple seeds: report mean +/ std, check reproducibility If sweeping a parameter: identify trends (monotonic, U shaped, plateau) Flag outliers or suspicious results Step 4: Generate Insights For each finding, structure as: 1. Observation : what the data shows (with numbers) 2. Interpretation : why this might be happening 3. Implication : what this means for the research question 4. Next step : what experiment would test the interpretation Step 5: Update Documentation If findings are significant: Propose updates to project notes or experiment reports Draft a concise finding statement (1 2 sentences) Output Format Always include: 1. Raw data table 2. Key findings (numbered, concise) 3. Suggested next experiments (if any)

analyze-results