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Related Questions
- Can you provide a real-life example of feature interaction, and how it influences the interpretation of permutation importance scores?
- How can feature interaction obscure the meaning of permutation importance values?
- Does the feature interaction effect persist in cross-validation settings? Or do the permutation importances stabilise?
- Under what conditions should users trust the permutation feature importances, and avoid interpretation flaws due to interactions?
- Can high dimensional sparse data amplify issues caused by feature interaction that degrade performance of permutation methods?
- Are all the major permutation feature selection and impact methods affected negatively to their performance when models get significantly affected by an existing problem of feature collinearity?
- Could variable selection algorithms sometimes mistakenly discover a relationship caused more artificially by a given underlying colinearity to existing inputs?
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