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Now, let's consider the effects in more detail. The squaring of the coefficients creates a smooth penalty. Unlike Lasso, which can zero out coefficients, Ridge just makes them small. This property is particularly useful when you believe that all features are somewhat relevant. The effect is to reduce the impact of all features, without completely ignoring any of them. Think of it as tuning all the dials slightly rather than turning some off entirely. This is also super helpful in reducing the impact of outliers in the data. Because Ridge regression is less sensitive to extreme values, it is able to provide a more stable model. **L2 regularization** can be preferred when you want to use all the features in your model but still want to reduce overfitting. This is useful when you suspect that multiple features are important, and each has a smaller contribution to the outcome. It's like finding a balance, ensuring that no single feature dominates the decision-making process. The primary benefit of **L2 regularization** is that it provides a more stable and accurate model, particularly when dealing with noisy data or datasets with multicollinearity.
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