We use hyperparameter optimization to increase the performance of the data miners, however most of the researchers don't use the parameters tuning as it needs a lot of time and effort.

In academic research, since we work with many datasets, in order to save time, we use default parameters and compromise on the accuracy. In industries, however, since there are few datasets and accuracy is required more than saving the time, so can we suggest that hyperparameter tuning is recommended for industrial purpose while for academic research, the default parameters are advisable?