I need a clarification about the Cross validation in parametric model such as a simple linear regression: Are the coefficients of the final model the mean of the coefficients estimated in each round? Only in this case a can see a reduction in variance of the model, infact
$var(\overline{\beta_i}) = \sigma^2_{\beta_i}/n$
Different is for non parametric models such as decision trees, which they have hyper parameters, in that case is just model selection. Am I interpreting correctly?


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