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**L1 regularization**, or Lasso, is all about that absolute value. When you apply Lasso, the penalty term is calculated as the sum of the absolute values of the coefficients, multiplied by a regularization parameter (often denoted as lambda or alpha). The lambda value controls the strength of the penalty; a higher lambda means stronger regularization. So, what's the magic here? The absolute value penalty has a knack for driving some of the coefficients to exactly zero. That's right, zero! This has a direct impact: it effectively performs feature selection. The model learns to ignore less important features by setting their corresponding coefficients to zero. This is incredibly useful when you're dealing with datasets that have many features, but not all of them are relevant. By doing this, **L1 regularization** simplifies the model by getting rid of unnecessary features. This can lead to a more interpretable model and can improve predictive performance, especially when there are many irrelevant features in the dataset. It's like finding a shortcut by discarding irrelevant information, leading your model directly to a good prediction path.
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