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It ensures predictions are stable with small input changes.
It increases the complexity of the model.
It reduces the amount of training data needed.
It guarantees high accuracy in all scenarios.
Understanding the Answer
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Sensitivity tells how much a prediction changes when a feature changes a little. Other options are incorrect because People think that making a model more sensitive means it is more complex; Some believe that a sensitive predictor means you need less 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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