Training a single model on three target variables is equivalent to training three separate models that have shared parameters except for the final layer (assuming a mean squared error loss in both cases), so training a single model effectively regularizes the three models. Whether or not this is a good thing will depend on the dataset, but in the limit of infinite data, three separate models will give you better overall performance than a single model since they won't be regularized.
michaelaalcorn t1_iyyhfhw wrote
Reply to [D] What is the advantage of multi output regression over doing it individually for each target variable by triary95
Training a single model on three target variables is equivalent to training three separate models that have shared parameters except for the final layer (assuming a mean squared error loss in both cases), so training a single model effectively regularizes the three models. Whether or not this is a good thing will depend on the dataset, but in the limit of infinite data, three separate models will give you better overall performance than a single model since they won't be regularized.