Core concept · Foundation

Offline Evaluation by ML Task

Match the metric to the decision and expose errors hidden by an average.

Content reviewed September 2026Read to the end

What you will learn

Match the metric to the decision and expose errors hidden by an average.

Decision trigger

Which error type or slice result would block deployment despite an average gain?

Interview impact

Task metrics gate model and architecture choices before expensive rollout.

Task to metric

1Decision task
2Pipeline stage
3Metric
4Slice
5Error review

Mental model

Evaluate each stage on the failure it can actually cause.

Candidate retrieval and final ranking are both evaluated with one generic accuracy score.

How it works

Classification uses precision/recall and threshold-aware analysis; retrieval uses Recall@K or MRR; ranking uses NDCG; regression uses MAE/RMSE/quantiles; forecasting adds rolling backtests.
Compare against a credible baseline, inspect slices, and classify errors by operational consequence.

Product consequences

Stage-specific evaluation prevents a good ranker from hiding a weak retriever.

Operational consequences

Evaluation datasets, baselines, and slice definitions need versioning and reproducible computation.

Alternatives

Use human evaluation for qualities not captured by reliable labels, with a defined rubric and sampling plan.
Use simulation only when its assumptions are validated against real outcomes.

Failure modes

An average hides a critical region or user cohort.
Confidence intervals and repeated comparisons are ignored.

Worked example

Search first checks candidate Recall@K, then NDCG@K for ordering, plus zero-result and permission failures.
An error review separates missing candidates, bad ranking, stale index, and unsafe results.

Interviewer probes

Which metric belongs to which stage?
What error taxonomy guides the next change?

Expected depth by level

Entry: Choose a task-appropriate metric and baseline.
Mid-level: Add slices and error taxonomy.
Senior+: Handle uncertainty, metric conflicts, and evaluation governance.
Role emphasis: Applied Scientist deepens statistical validity; SWE-ML ensures reproducible pipelines.

System Design prerequisites

Observability

Sources and review notes

These sources support the technical claims on this page. Product-specific examples remain Engineering Foundry teaching scenarios.

Classification: accuracy, recall, precision, and related metricsGoogle for Developers · official documentation · reviewed 2026-09-04
Task-sensitive classification metrics and the limitations of accuracy on imbalanced data.
Patterns of Trustworthy Experimentation: Pre-Experiment StageMicrosoft Research · first-party engineering/science · reviewed 2026-09-04
Randomization units, guardrails, network effects, and pre-experiment validity checks.

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Sources and review notes

These sources support the technical claims on this page. Product-specific examples remain Engineering Foundry teaching scenarios.

Classification: accuracy, recall, precision, and related metricsGoogle for Developers · official documentation · reviewed 2026-09-04

Task-sensitive classification metrics and the limitations of accuracy on imbalanced data.

Patterns of Trustworthy Experimentation: Pre-Experiment StageMicrosoft Research · first-party engineering/science · reviewed 2026-09-04

Randomization units, guardrails, network effects, and pre-experiment validity checks.

Decision trigger

Interview impact

Mental model

How it works

Product consequences

Operational consequences

Alternatives

Failure modes

Worked example

Interviewer probes

Expected depth by level

System Design prerequisites

Practice this concept

Sources and review notes

Finish this lesson

Decision trigger

Interview impact

Mental model

How it works

Product consequences

Operational consequences

Alternatives

Failure modes

Worked example

Interviewer probes

Expected depth by level

System Design prerequisites

Practice this concept

Sources and review notes

Finish this lesson