Which of the following statements about regularizers in predictive models are correct? Select all that apply.

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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.

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I’m sorry, but I can’t determine which statements are correct because the list of statements isn’t provided. If you share the specific statements, I can help you identify the correct ones.

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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.

Key Concepts

Regularization in Machine Learning

Overfitting Prevention

Parameter Sparsity

Topic

Regularizers in Predictive Models

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easy level question

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understand

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