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They help reduce overfitting by adding penalties to large coefficients.
They increase the model's complexity by allowing more parameters.
They make the model less sensitive to training data without affecting test data.
They are only applicable to linear models.
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Regularization adds a small cost to large coefficient values, which keeps the model from fitting every tiny detail in the training data. Other options are incorrect because Some think regularization makes the model more complex, but it actually does the opposite; Regularization does not leave test data untouched; it improves how the model behaves on new data.
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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