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L1 regularization encourages sparsity in the model parameters.
L2 regularization always results in a model that has fewer parameters than the original model.
Regularizers can help prevent overfitting by penalizing complex models.
Regularization techniques can be used interchangeably without affecting model performance.
L1 regularization can lead to some parameters being exactly zero.
Understanding the Answer
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L1 regularization (also called Lasso) pushes some coefficients exactly to zero, making the model sparse. Other options are incorrect because The misconception is that L2 regularization removes parameters; People think all regularizers work the same.
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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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