Concept Shift: When Relationships Change
Understanding how distribution shift affects model evaluation requires distinguishing between different types of shifts. While covariate shift involves changes in the distribution of input variables (the X's), concept shift β sometimes called "conditional shift" or "label shift" β refers to changes in the underlying relationship between inputs and outputs. In concept shift, the mapping from features to labels or targets evolves over time, even if the input space itself remains unchanged. This means that the same input might now correspond to a different output than before, fundamentally altering what a model is expected to learn and predict.
When facing covariate shift, models are evaluated on data where the distribution of input features has changed, but the relationship between features and labels stays the same. For instance, if you trained a loan approval model on one regionβs applicants and then evaluated it on another region with slightly different applicant demographics, your modelβs predictions may degrade, but the evaluation metrics still reflect the same definition of "good" and "bad" applicants. The offline evaluation remains informative, though possibly optimistic or pessimistic depending on how the input changes affect the model.
Under concept shift, the relationship between features and labels has changed. Using the same loan approval example, imagine that the criteria for approving loans have been updated, so that income is now weighted less and employment stability more. Even if the applicant demographics remain constant, the modelβs previous understanding of what constitutes a "good" applicant is now outdated. Offline evaluation using historical data will be misleading, since it measures performance against an obsolete target. The metrics no longer reflect the modelβs ability to make correct predictions in the new context.
This distinction is critical because concept shift can make offline evaluation nearly meaningless. If the relationship between inputs and outputs has changed, then past data β no matter how much or how carefully collected β no longer represents the task the model must solve. Evaluation metrics computed on this data will not predict future performance, and may even suggest a model is effective when it is not. Covariate shift affects the representativeness of the test set, but concept shift undermines the very definition of what "correct" means, rendering previous evaluation procedures unreliable.
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Understanding how distribution shift affects model evaluation requires distinguishing between different types of shifts. While covariate shift involves changes in the distribution of input variables (the X's), concept shift β sometimes called "conditional shift" or "label shift" β refers to changes in the underlying relationship between inputs and outputs. In concept shift, the mapping from features to labels or targets evolves over time, even if the input space itself remains unchanged. This means that the same input might now correspond to a different output than before, fundamentally altering what a model is expected to learn and predict.
When facing covariate shift, models are evaluated on data where the distribution of input features has changed, but the relationship between features and labels stays the same. For instance, if you trained a loan approval model on one regionβs applicants and then evaluated it on another region with slightly different applicant demographics, your modelβs predictions may degrade, but the evaluation metrics still reflect the same definition of "good" and "bad" applicants. The offline evaluation remains informative, though possibly optimistic or pessimistic depending on how the input changes affect the model.
Under concept shift, the relationship between features and labels has changed. Using the same loan approval example, imagine that the criteria for approving loans have been updated, so that income is now weighted less and employment stability more. Even if the applicant demographics remain constant, the modelβs previous understanding of what constitutes a "good" applicant is now outdated. Offline evaluation using historical data will be misleading, since it measures performance against an obsolete target. The metrics no longer reflect the modelβs ability to make correct predictions in the new context.
This distinction is critical because concept shift can make offline evaluation nearly meaningless. If the relationship between inputs and outputs has changed, then past data β no matter how much or how carefully collected β no longer represents the task the model must solve. Evaluation metrics computed on this data will not predict future performance, and may even suggest a model is effective when it is not. Covariate shift affects the representativeness of the test set, but concept shift undermines the very definition of what "correct" means, rendering previous evaluation procedures unreliable.
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