Applied Predictive Modeling¶
Kuhn, M., & Johnson, K. (2013). Applied Predictive Modeling. Springer.
Cited by¶
2 citations across 2 artifacts.
Each citation links to the sentence it supports in the citing article.
Primes¶
- Impartiality
- Kuhn and Johnson (2013) treat this as a generic estimator property when they characterize unbiased predictive models as ones whose expected error is independent of nuisance variation in training data.
This sourceTreats unbiasedness as a generic estimator property of predictive models: expected prediction error must be independent of nuisance variation in training data — the impartiality condition applied to machine-learning estimators rather than classical statistics.
- Kuhn and Johnson (2013) treat this as a generic estimator property when they characterize unbiased predictive models as ones whose expected error is independent of nuisance variation in training data.
- Validation
- This distinction is foundational to learning from failures, adapting systems, and transferring knowledge across contexts, as Kuhn and Johnson (2013) emphasize in their treatment of predictive-model validation as the bridge between training-time intent and deployment-time behavior.
This sourceTreats unbiasedness as a generic estimator property of predictive models: expected prediction error must be independent of nuisance variation in training data — the impartiality condition applied to machine-learning estimators rather than classical statistics.
- This distinction is foundational to learning from failures, adapting systems, and transferring knowledge across contexts, as Kuhn and Johnson (2013) emphasize in their treatment of predictive-model validation as the bridge between training-time intent and deployment-time behavior.
Verification¶
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Does it back the claim? Not recorded. Neither this nor any other of the 2 citations of this work carries a recorded support check.
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