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Question & Answer
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It reduces the coefficients of less important features to zero, promoting sparsity.
It increases the complexity of the model by allowing more features to be included.
It has no effect on the model's performance but increases computation time.
It uniformly scales all coefficients by the same factor.
Understanding the Answer
Let's break down why this is correct
When the penalty is stronger, the model pushes small coefficients toward zero. Other options are incorrect because Some think a stronger penalty lets more features stay in the model, but actually it removes them; Increasing the penalty does not just waste time; it usually improves accuracy by reducing noise.
Key Concepts
Regularizers in Predictive Models
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Deep Dive: Regularizers in Predictive Models
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Definition
Regularizers are functions that control the sensitivity of predictive models by penalizing complex or sensitive parameter configurations. Common regularizers include `2 (ridge) and `1 (Lasso) regularization, which encourage stable and sparse parameter solutions.
Topic Definition
Regularizers are functions that control the sensitivity of predictive models by penalizing complex or sensitive parameter configurations. Common regularizers include `2 (ridge) and `1 (Lasso) regularization, which encourage stable and sparse parameter solutions.
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