Spearman's rank correlation coefficient
Spearman's rank correlation coefficient is a rank-based statistic that measures the strength and direction of the monotonic association between two variables.
Outlier Detection in Python
Spearman correlation is a rank-based measure of the strength and direction of a monotonic association between two variables.
- How it is used
- Compares outlier-detector rankings and guides the grouping of associated features into subspaces.
- How deep it goes
- Defines correlation of ranks and demonstrates score filtering and feature grouping, without deriving a coefficient formula.
- Examples
- Uses abalone detector scores and baseball features, including the reported 0.94 correlation between At bats and Runs.
- What you can do after
- Apply it via pandas to compare detector rankings or construct correlation-guided feature subspaces.
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Machine Learning System Design
A correlation coefficient that measures the monotonic association between ranked variables.
- How it is used
- Uses ranked offline metrics to select proxies for online search outcomes and guide initial A/B-test planning.
- 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.
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Related concepts
- correlationkind of · Correlation is a statistical relationship describing how two or more variables or quantities vary together, including the strength and direction of their association, without by itself implying causation.
- ordinal datauses · Ordinal data consists of categorical values with a meaningful order or rank, without necessarily having equal intervals between categories.
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