Label Drift (a.k.a. Target Drift)#
A change over time in the distribution of the target variable.
Important
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What it is#
Label drift (target drift) is a change in the distribution of the target itself — the class balance or outcome mix shifts between training and production, \(p_{\text{train}}(y) \neq p_{\text{prod}}(y)\), even when the feature-to-label relationship may be unchanged. A fraud rate that creeps from 1% to 3% is label drift.
How it differs#
Like covariate drift it is a dataset shift, but it moves p(y) rather than p(x) or p(y | x). Because most classifiers implicitly assume the base rate they trained on, a shifted target distribution can throw off calibrated probabilities and thresholds even if each input still maps to the right answer.
Detecting and fixing it#
Monitor the label or prediction distribution over time (PSI on predicted classes, tracked class proportions). Fixes include recalibrating decision thresholds to the new base rate, reweighting or resampling to the current mix, and retraining on recent labels.
Theme: Distribution Shift & Drift · All terminology
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Mind map — connected ideas
Covariate Drift (a.k.a. Covariate Shift) · Concept Drift · Dataset Shift · PSI (Population Stability Index) · Data Drift · Drift Detection
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More in Distribution Shift & Drift
Cardinality in Categorical Data · Categorical Drift · Categorical Explosions · Classifier Two-Sample Tests (C2STs) · Concept Drift · Covariate Drift (a.k.a. Covariate Shift) · Data Drift · Dataset Shift · Drift Detection · Drift Guardrails · Energy Distance · Jensen–Shannon (JS) Divergence · KS shift (Kolmogorov–Smirnov shift) · Kullback–Leibler (KL) Divergence
See also
Source article Adapted (context, re-expressed) in our own words from: Label Drift (a.k.a. Target Drift) (insightful-data-lab.com).