inno-experiment-analysis

To analyze ML experiment results, you can load CSV or JSON files to automatically validate data, run statistical tests, generate visualizations, and draft publication-ready Results sections for your research paper.

You can run statistical tests including t-tests and ANOVA to compare model performance across multiple datasets, with automatic normality and variance checks and effect-size reporting to ensure reproducibility.

You can generate publication-ready visualizations from experimental data by running an automated analysis command on your CSV or JSON result files, which produces charts and structured report artifacts for paper writing.

No external dependencies are required to run statistical analysis on your experiment results, as the Skill operates with minimal setup and enforces data validation, normality checks, and effect-size reporting internally.

You can automate drafting the Results section by processing your experimental data files, which applies statistical testing and visualization generation to produce structured text integrated directly into a paper-writing workflow.