A report turns measurements, analyses, and system outcomes into evidence for evaluation and decisions. In machine learning, it connects model performance with business goals and communicates results to teammates and stakeholders. section 12.0
A useful report should:
- Compare offline results with the online effect they are expected to predict, while distinguishing direct business metrics from proxy metrics. section 12.0
- Show overall metrics alongside segment-level metrics and error analysis, rather than hiding important differences behind one aggregate score. section 12.4.1
- Record experiment status, investigate failures promptly, and stop an experiment when its results become unreliable. section 12.3
- Preserve validation and test metrics, visualizations, error analyses, and experiment information as reviewable artifacts before release. section 10.1.1
Forecasting report
A sales-forecasting report can compare a unified model with category-specific baselines across quantiles, percentage errors, biases, seasons, regions, and product categories. It can then translate expected metric changes into missed-profit, overstock, and revenue estimates, such as a projected pilot increase of 0.3% to 0.7%. section 12.4.1
Reporting is also a reliability practice. Carefully track experiment outputs, protect the best results from accidental overwriting, and make conclusions reproducible enough for technical and organizational scrutiny. section 12.3 section 10.1.1
