How does the parameter alpha in lasso regression and ridge regression affect the results?

The parameter alpha in lasso regression and ridge regression determines the level of regularization applied to the model.

In ridge regression, increasing alpha will increase the shrinkage of coefficients towards zero, resulting in a simpler model with smaller coefficients. This can help reduce overfitting and improve the model's generalization ability.

In lasso regression, increasing alpha also increases the level of regularization, but it can also lead to coefficients being exactly zero. This makes lasso regression useful for feature selection, as it can effectively remove less important features from the model.

In both cases, the optimal value of alpha needs to be determined through cross-validation, as too high or too low values can result in underfitting or overfitting.