Feature Engineering Report
Report type key: feature_engineering_markdown
Configuration type: Analytic
Group: Analytic
ML-ready features: transforms, scaling, encoding, dimensionality reduction, and an engineered dataset summary.
How to get it
- Create and save an Analytic configuration.
- Enable: Analysis & LLM → Feature Engineering.
- Run the job and wait until completed.
- Open Reports (
/reports), select that job, and choose this type.
Markdown viewer: /markdown/JOB_ID/feature_engineering_markdown
API: GET /reports/JOB_ID/feature_engineering_markdown
The family was off, mappings were incomplete, or the step failed. Check job logs and Analysis & LLM. Skipped Financial KPIs mean unmapped fields, not zeros.
How to read
Use this to inspect what was derived before clustering or models. Optional merge of time-series features when that module is on.
Companion artifacts
| Key | Artifact |
|---|---|
feature_engineering_markdown | Markdown report |
feature_engineering_notebook | Jupyter notebook |
feature_engineering_graphs | PNG folder |
Markdown, notebook, and graphs for the same family usually land together. JSON feeds Analysis Insights.
Related
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