results-analysis

To generate a paper-ready results section from experiment data, load CSV, JSON, TensorBoard logs, or pickle files for statistical validation, performance comparison, and visualization creation, outputting a markdown report draft.

Yes, TensorBoard logs can be used for statistical validation in ML papers. The skill loads these logs alongside CSV, JSON, and pickle files to perform t-tests, ANOVA, and Wilcoxon tests for rigorous performance comparison.

The best way to compare ML model performance statistically is running t-tests, ANOVA, or Wilcoxon tests with multiple-comparison corrections. This validates experimental results and generates figures visualizing performance differences.

Yes, this workflow supports creating visualizations from Python pickle files. It loads pickle data, performs statistical analysis, and generates figures alongside a markdown report draft ready for manuscript inclusion.

To perform multiple-comparison corrections on ANOVA results, apply statistical validation procedures to your experimental data. The skill handles these corrections automatically to ensure rigor in ML paper results sections.

A limitation when analyzing TensorBoard logs for markdown reports is ensuring data quality and proper loading. The skill requires structured experimental data to perform statistical tests and generate accurate figures and draft text.