analyze-results

To compare ML experiment results, this Skill parses JSON and CSV outputs to build structured, comparable summaries. It organizes performance trends by model type, hyperparameters, and data configurations to reveal statistically significant differences.

Yes, you can generate reproducible statistics by parsing your experiment outputs. The Skill computes mean +/- std, calculates delta versus baseline, and detects outliers to ensure your ML experiment analysis remains statistically sound and reproducible.

The best way to translate machine learning experiment data into actionable insights is by generating structured comparison reports. This Skill provides concise observations, interpretations, implications, and concrete next steps for model selection and future experiments.

Yes, the analysis tool supports both JSON and CSV file formats. It directly parses these output files from your experiment directory to automatically generate concise comparison reports with statistical summaries and baseline deltas.

To detect outliers in your hyperparameter tuning data, this Skill applies statistical analysis to your parsed JSON or CSV experiment results. It identifies performance anomalies and calculates delta versus baseline to inform your model selection decisions.