P3-9.2 Why Should Some Problems Remain Comparison Reports?¶

Section ID: P3-9.2 Version: v2026.09.20

“How does current flow differ from its reference?” requires observations and comparison evidence, not necessarily a future-failure prediction. A comparison report communicates observed differences and their limitations. If it answers that question, it is complete without becoming a prediction system and can remain useful as long as the purpose remains.

What one report row can establish¶

Reuse fictional row A from P3-8.4. One row is a comparison window containing 20 recent completed operations. Retain its applicability assumptions: required measurements are available; baseline and recent data match in type, operating conditions, units, and segment definitions; and no separate allowable-limit violation exists.

The decline rule includes equality and counts each operation once. The −0.35 baseline difference and −0.30 within-operation decline boundary compare different things. An unknown cause_label means neither that no cause exists nor that the case is normal.

The observation statement is: “The late-period mean for 20 recent operations was 0.35 L/min below baseline, and 14/20 operations met the within-operation decline condition.” Add the limitation that occurrence order and cause are unverified. Do not turn 70% into a failure probability or describe 14 consecutive declines.

Include the policy when adding operational decisions¶

For this row, review-v1 matches R1 because −0.35 ≤ −0.30 and 0.70 ≥ 0.60. Record warning_level=caution, review_needed=1, and priority_band=first. These come from the fictional assignment policy in P3-8.4, not from calculating a cause label or failure probability.

The report can thus explain a current difference and support a specific checking action. Automatically adding “high review priority because the difference is large” without a policy would mix observations with assignment decisions. An ordered review queue may use the report's evidence but is a separate policy output.

Unknown causes differ from unknown future outcomes¶

Without cause_label, these data alone are insufficient for learning confirmed cause classification. That does not block every prediction question. If each historical operation's inputs available at completion are consistently linked to failure outcomes over the following seven days, supervised prediction of “failure within seven days” can be considered without cause names.

The label requirement here concerns supervised prediction. It does not mean every analysis or machine-learning approach requires confirmed outcome labels. Also, review_needed is an operational policy result; using it as the ground truth for failure changes what is being learned.

The current A row has no seven-day outcomes linked to individual operations. Copying its decline_ratio=0.70 cannot create future-failure labels for those operations. Check separately whether the individual records exist. Define inclusion of outcome-window endpoints and tracking gaps, and do not fill incomplete follow-up with non-failure zero.

Choosing Outputs That Match the Comparison Evidence¶

flowchart TD
    A["Current comparison question"] --> B["Report: difference, counts, limitations"]
    B --> C["Apply explicit policy if review is needed"]
    D["Separate future-outcome question"] --> E["Check individual inputs, outcome labels, evaluation"]
    C --> F["Keep the report; prepare prediction separately"]
    E --> F

If future prediction is the purpose, do not claim the report answers that question instead. Conversely, a comparison question already answered need not be called a failed prediction task because labels are missing. Keep the report while collecting outcome records to prepare a separate prediction problem.

Answer two requests separately¶

You receive two requests about A: ① “Summarize recent change and what to check today”; ② “Predict whether each operation in A will fail within the following seven days.” Separate the answer supported by current data from additional records needed.

Explanation: For ①, report −0.35 L/min, 14/20 operations, and the unverified cause, then connect R1 of review-v1 to checking raw records and setting histories. For ②, the window summary is insufficient: check individual operation IDs, inputs at prediction time, consistently defined seven-day outcomes and completion status, and evaluation data separate from training. Missing cause names alone do not make ② impossible.

Checklist¶

• Can you separate A's observations, interpretive limits, and policy outputs?
• Can you identify the unit difference between a comparison window and individual operations, and the record differences between current comparison and future prediction?
• Can you distinguish cause labels from failure-outcome labels and explain label requirements in the supervised-learning context?

Sources and references¶

• Google, Machine Learning Glossary — Reference for label and supervised machine learning concepts. The comparison example and review-v1 are fictional constructions for this book. Accessed: 2026-09-20.