Analyze ML experiment results to compute statistics and generate comparison tables.
npx skills add https://github.com/xqinag/ARIS-new --skill analyze-results-xqinag
Please help me install this Agent Skill. Skill: analyze-results Source: https://github.com/xqinag/ARIS-new/tree/main/skills/analyze-results Command: npx skills add https://github.com/xqinag/ARIS-new --skill analyze-results-xqinag
Analyze ML experiment results, compute statistics, and generate comparison tables to uncover actionable insights.
Run the skill on your results directory to produce a comparison summary and key insights.
High-intent search queries and answers about installing and using this skill.
To compute statistics for ML experiment results, aggregate metrics across seeds and configurations to calculate mean, standard deviation, and confidence intervals. This identifies metric deltas versus baselines and generates structured comparison tables.
Yes, you can parse JSON and CSV formats to compare models, hyperparameters, and data splits. The analysis aggregates metrics across these configurations to produce concise insights and structured comparison tables.
The best way to derive insights from ML experiment suites is to compute statistics across configurations and identify deltas versus baselines. This generates concise observations, interpretations, and implications to guide model decisions.
Yes, this analysis works with experiment suites containing multiple configurations. It parses JSON and CSV results to aggregate metrics, compute standard deviations, and compare model performance across different seeds and setups.
Generate comparison tables for ML metrics by parsing JSON or CSV experiment results. The process aggregates metrics across seeds, computes confidence intervals, and identifies performance deltas versus baselines to structure the tables.
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