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¶
This reference passed the adversarial substantiation pipeline: it was checked to exist and to support the claim it is attached to. See how references were verified.
Registry ID ref:0f855c92dcc7 · see in the full table