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Cross‐validation is the standard method for hyperparameter tuning, or calibration, of machine learning algorithms. The adaptive lasso is a popular class of penalized approaches based on weighted L1‐norm penalties, with weights derived from an initial estimate of the model parameter. Although it violates the paramount principle of cross‐validation, according to which no information from the hold‐out...