A Unifying View on Dataset Shift in Classification¶
Moreno-Torres, J. G., Raeder, T., Alaiz-Rodríguez, R., Chawla, N. V., & Herrera, F. (2012). A Unifying View on Dataset Shift in Classification. Pattern Recognition, 45(1), 521-530.
Cited by¶
3 citations across 3 artifacts.
Each citation links to the sentence it supports in the citing article.
Primes¶
- Concept Drift
- Consider a deployed binary classifier $f: X \to \{0,1\}$ trained on data drawn from a joint distribution $P_0(x, y)$, decomposed as $P_0(x)\,P_0(y \mid x)$.
This sourceFormalizes dataset shift via the joint P(x,y) = P(x)P(y|x), separating covariate shift, prior-probability shift, and concept shift.
- Consider a deployed binary classifier $f: X \to \{0,1\}$ trained on data drawn from a joint distribution $P_0(x, y)$, decomposed as $P_0(x)\,P_0(y \mid x)$.
- Data Drift
- The deployment distribution drifts — input statistics shift (covariate or feature drift), the input-output relationship shifts (concept drift), or the output base rate shifts (label drift).
This sourceTaxonomizes dataset shift into covariate shift, prior-probability (label) shift, and concept shift — the three axes along which the deployment distribution drifts.
- The deployment distribution drifts — input statistics shift (covariate or feature drift), the input-output relationship shifts (concept drift), or the output base rate shifts (label drift).
Mechanisms¶
- Decision Tree Pruning
- It misleads when the holdout is not representative of where the model will be used — prune against a validation set drawn from the wrong distribution and you optimize for the wrong world, cutting branches that would have mattered in production.
This sourceDefines dataset shift as a mismatch between development data and the distribution encountered in unseen real-world use.
- It misleads when the holdout is not representative of where the model will be used — prune against a validation set drawn from the wrong distribution and you optimize for the wrong world, cutting branches that would have mattered in production.
Verification¶
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