inno-experiment-analysis

To analyze ML experiment results and write a Results section, load your CSV or JSON logs to run statistical tests, generate publication-quality figures, and draft the analysis. This workflow outputs an analysis report, a draft results section, and visualization specs.

You can run statistical tests on machine learning results across multiple datasets and ablations by loading your experiment logs. The process enforces statistical prerequisites like normality and variance checks, computes effect sizes, and applies multiple-comparison corrections to demonstrate robustness.

The best way to generate publication-quality visualizations from AI experimental logs is to process your results through a structured analysis workflow. This validates the underlying data and outputs specific visualization specifications ready for publication.

You do not need to manually check normality and variance before comparing ML model performance. The analysis workflow automatically enforces these statistical prerequisites, computes effect sizes, and applies multiple-comparison corrections to ensure rigorous statistical comparisons.

Yes, you can use CSV and JSON logs to automatically draft a paper's Results section. By loading your experimental results, the workflow performs the necessary statistical analyses and generates a results-draft.md file alongside analysis reports and visualization specs.

A limitation when processing raw experiment logs into publication-ready analyses is that the input must be structured as CSV or JSON logs. The workflow focuses strictly on statistical validation, visualization, and drafting, meaning prior data logging formatting is required.