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Question & Answer
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Confounding variables can create a false sense of sensitivity by altering the observed effect of a predictor.
Confounding variables have no impact on the sensitivity of predictors.
Sensitivity analysis is only concerned with independent variables, thus confounding variables are irrelevant.
All predictors will show increased sensitivity when confounding variables are present.
Understanding the Answer
Let's break down why this is correct
A confounding variable is a hidden factor that influences both a predictor and the outcome. Other options are incorrect because The idea that confounders do nothing is a misconception; Sensitivity analysis looks at how changes in any variable affect the outcome.
Key Concepts
Sensitivity of Predictors
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Definition
The sensitivity of a predictor measures its responsiveness to changes in input features. Insensitive predictors exhibit stability in their predictions when inputs are close. Sensitivity is crucial for generalization and performance, especially with limited training data.
Topic Definition
The sensitivity of a predictor measures its responsiveness to changes in input features. Insensitive predictors exhibit stability in their predictions when inputs are close. Sensitivity is crucial for generalization and performance, especially with limited training data.
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