The regularization parameter is a hyperparameter used in regression models, particularly in Ridge Regression, to control the trade-off between fitting the data well and keeping the model coefficients small. By adjusting this parameter, one can manage overfitting and improve the model's generalization to new data. It essentially adds a penalty for large coefficients to the loss function, influencing how much the model should prioritize complexity versus accuracy.
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