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To increase the complexity of the model
To prevent overfitting by adjusting the loss function
To decrease the amount of data needed for training
To speed up the training process
Understanding the Answer
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Regularizers add a penalty to the loss function, which discourages large weights. Other options are incorrect because People sometimes think regularizers make the model more complex, but they actually do the opposite; Regularizers do not reduce the amount of data needed.
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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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