Metricslike

Metricslike is a term used in data analysis and performance measurement to describe a class of metrics that are related to a core metric but are not identical copies. The term is informal and varies by domain. Metricslike are intended to capture similar phenomena as the target metric or to provide alternative viewpoints when the core metric is unavailable, noisy, or biased.

Common characteristics: they are correlated with the core metric, but differ in data source, unit, time window, or aggregation. They are used to test robustness, monitor different aspects, or facilitate benchmarking across contexts.

Calculation and examples: If the core metric is conversion rate, metricslike could include micro-conversion rates (such as add-to-cart rate), engagement proxies (time on site, pages per visit), or revenue per user. In ML evaluation, metricslike might be alternative loss functions or surrogate objectives. In operations, metricslike could be service level indicators that approximate customer satisfaction.

Relation to related concepts: proxy metrics, derived metrics, and composite indicators; risk of misinterpretation; ensure alignment with business goals.

Best practices: document definitions, compute in parallel, examine correlation and monotonicity, avoid cherry-picking, communicate limitations.