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Question & Answer
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The model is insensitive to changes in input features.
The input features are highly correlated.
The model has overfitted to the training data.
The model is using an excessively complex algorithm.
Understanding the Answer
Let's break down why this is correct
The model does not change its output much when the input changes. Other options are incorrect because The idea that highly correlated features make a model insensitive is a misconception; Overfitting means the model memorizes training data and often gives erratic predictions on new data.
Key Concepts
Sensitivity of Predictors
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Deep Dive: 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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