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Question & Answer
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Sensitive predictors adapt quickly to small changes, improving generalization.
Insensitive predictors remain stable under small changes, aiding generalization.
Sensitivity is irrelevant to generalization and affects only accuracy.
All predictors have the same level of sensitivity regardless of their design.
Understanding the Answer
Let's break down why this is correct
A predictor that does not change much when the input changes is called insensitive. Other options are incorrect because The idea that a very reactive predictor is always better is a misconception; Thinking that sensitivity only matters for accuracy ignores how a model behaves on new data.
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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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