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Sensitivity of Predictors
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A data scientist is developing a machine learning model to predict housing prices based on various features, such as location, size, and age of the property. After testing the model, she notices that small changes in the size of the house lead to large fluctuations in predicted prices. What does this indicate about the sensitivity of the predictor used in her model?

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A

The predictor is highly sensitive, which may lead to overfitting.

B

The predictor is insensitive, ensuring stable predictions across similar inputs.

C

The predictor has a fixed response, making it reliable for all input variations.

D

The predictor is robust and does not depend on the input features.

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When a model reacts a lot to tiny changes, it means the predictor is very sensitive. Other options are incorrect because The misconception is that an insensitive predictor keeps predictions stable; The misconception is that a fixed response means reliability.

Key Concepts

Sensitivity of Predictors
Overfitting in Machine Learning
Generalization of Models
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Sensitivity of Predictors

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medium level question

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Definition
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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