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An example of where you might use Ridge regression is in financial modeling, where various financial indicators (interest rates, inflation, etc.) all influence stock prices. Instead of trying to eliminate certain indicators, Ridge regression is used to scale down the impact of all indicators, resulting in a more reliable model. However, one of the primary drawbacks is the lack of feature selection. Because it doesn't drive coefficients to zero, it doesn't give you a clear sense of which features are most important. Furthermore, with very strong regularization, the coefficients can be shrunk too much, which leads to bias. This means you have to choose your lambda carefully, and it often requires tuning through cross-validation or other techniques to find the best model parameters.
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