Ridge and Lasso regression are often perceived as more complex versions of linear regression. In reality, the prediction model remains exactly the same. What changes is the training objective. By adding a penalty on the coefficients, regularization forces the model to choose more stable solutions, especially when features are correlated. Implementing Ridge and Lasso step by step in Excel makes this idea explicit: regularization does not add complexity, it adds preference.
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