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Spearman's rank correlation coefficient

As explained in Machine Learning System Design

A correlation coefficient that measures the monotonic association between ranked variables.

Spearman's rank correlation coefficient measures how consistently two ranked measures move together. In search evaluation, it links offline ranking metrics with online business metrics such as CTR and CVR. section 12.4.2

A practical measurement workflow uses it to:

  • Collect SERPs from the current search algorithm and calculate offline metrics such as NDCG and MRR. section 12.4.2
  • Measure CTR and CVR across different time periods, then identify which offline metric correlates most strongly with each online metric. section 12.4.2
  • Use the selected offline metric in a simple regression model to provide an initial estimate of the minimum detectable effect for an A/B test. section 12.4.2

Search-metric calibration

A stock-photo search team compares human- and implicitly labeled NDCG and MRR with observed CTR and CVR. It uses the strongest relationship to guide future experiment planning, while deferring behavioral simulation as a more complex refinement. section 12.4.2

How Machine Learning System Design covers it

How deep it goes
Applies correlation across time periods and feeds the strongest relationship into regression, but defers behavioral simulation.
Examples
Compares human- and implicit-feedback NDCG and MRR against search-result CTR and CVR.
What you can do after
Evaluate offline metrics as proxies for online outcomes and estimate an experiment's initial minimum detectable effect.
How it connects
Connects NDCG, MRR, CTR, CVR, offline evaluation, regression models, minimum detectable effect, and A/B testing.

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Related concepts

This explainer was generated by Manning's knowledge graph from the text of Machine Learning System Design. The section links point to the parts of the book it draws on.