Before revision:  It follows that ML specifically targets estimation risk when choosing the asset weights, and that "off-the-shelf" ML algorithms obtain optimal portfolios taking parameter uncertaintyinto account. Linear regression is a special case of the proposed ML framework , equivalent to the traditional approach . Standard results from the machine learning literaturemay be used to derive conditions for when ML algorithms improve upon linear regression . Based on simulation studies and several datasets, I find that ML significantly reduce estimation risk compared to  the traditional approach and several shrinkage approaches proposed in the literature .
After revision:  It follows that ML specifically targets estimation risk when choosing the asset weights, and that "off-the-shelf" ML algorithms can be used to estimate the optimal portfolio in the presence of parameter uncertainty. The framework nests the traditional approach . Standard results from the machine learning literaturemay be used to derive conditions for when ML algorithms improve upon linear regression . Based on simulation studies and several datasets, I find that ML significantly reduce estimation risk compared to the traditional approach and several shrinkage approaches proposed in the literature .