Learning Path
Question & Answer
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By adding a penalty equal to the absolute value of the coefficients
By increasing the number of features used
By removing all features that contribute to the model
By minimizing the sum of squared errors only
Understanding the Answer
Let's break down why this is correct
It adds a penalty that equals the sum of the absolute values of the coefficients. Other options are incorrect because Adding more features usually increases complexity and can worsen overfitting; Removing all features would leave nothing to predict.
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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