analyze-results

Analyze ML experiment results from JSON/CSV to compute statistics and generate insights.

14.4k|1.3k|Updated Mar 10, 2026

Search Tags:#machine learning #statistics #data interpretation #experiment analysis #ml #results

Ready to run

npx skills add https://github.com/wanshuiyin/Auto-claude-code-research-in-sleep --skill analyze-results-wanshuiyin

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: analyze-results
Source: https://github.com/wanshuiyin/Auto-claude-code-research-in-sleep/tree/main/skills/analyze-results
Command: npx skills add https://github.com/wanshuiyin/Auto-claude-code-research-in-sleep --skill analyze-results-wanshuiyin

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill helps researchers and engineers understand the outcomes of machine learning experiments by analyzing results, computing statistics, and generating actionable insights.

Core Features & Use Cases

Result Analysis: Parses and consolidates experimental data from various file formats (JSON, CSV).
Statistical Computation: Calculates key metrics, standard deviations, and relative improvements against baselines.
Insight Generation: Identifies trends, flags outliers, and suggests interpretations and next experimental steps.
Use Case: After running a hyperparameter sweep, use this Skill to compare the performance of different configurations, identify the optimal settings, and understand why they performed best.

Quick Start

Use the analyze-results skill to interpret the experiment data found in the 'results/final_run' directory.

Frequently Asked Questions about analyze-results

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I analyze machine learning experiment results from JSON and CSV files?▼

To analyze machine learning experiment results, you can parse JSON and CSV files to consolidate experimental data, calculate key metrics, and generate comparison tables. This process computes statistics and derives actionable insights for ML research workflows.

What is the best way to compare ML hyperparameter sweep configurations?▼

The best way to compare ML hyperparameter sweep configurations is to calculate relative improvements against baselines and compute standard deviations. This identifies optimal settings, flags outliers, and generates structured observations about why specific configurations performed best.

How does identifying trends and outliers work in ML data interpretation?▼

Identifying trends and outliers in ML data interpretation works by parsing experimental results, computing statistics, and structuring findings. It flags data anomalies and suggests interpretations, implications, and next experimental steps to guide subsequent ML research workflows.

Can I use scripts to compute statistics and derive insights for ML experiments?▼

Yes, you can use scripts to compute statistics and derive insights for ML experiments. Scripts parse experimental data from JSON or CSV formats, calculate metrics, and structure findings into observations, interpretations, implications, and actionable next steps.

What statistics can I compute when interpreting experimental data for ML research?▼

When interpreting experimental data for ML research, you can compute key metrics, standard deviations, and relative improvements against baselines. These statistics help identify trends, flag outliers, and generate comparison tables for different experimental configurations.