# Comparison of machine learning approaches for a topic in a scientific paper

As part of my master's thesis, I have made a prediction of data with approaches of machine learning in a topic where are no papers yet. The topic is a regression problem for which several machine learning approaches have been tested: SVR, RandomForestRegressor, GradientBoosting, DecisionTrees, Artificial Neural Networks, LSTMs, K-NearestNeighbors..

It has been shown that useful results can be achieved with all approaches. Which of the methods is the best depends heavily on the parameter configuration.

It would be too much work to test any parameter configuration for any approach. Besides, I think it would be uninteresting to show dozens of plots about the parameter tuning tests, because they also depend heavily on the respective data sets.

So how do you typically compare different types of machine learning approaches in scientific papers that are heavily dependent on the parameters? Just play a bit with grid search and randomized search until you are satisfied with the parameter configuration?

• Good question. The specific accuracies typically depend on how much we are familiar with the method and how much time and passion we invest in parameter tuning. For a scientific paper, it would be optimal to provide details on these points, e.g. which parameters were tuned and how much time was spent. – Michael M May 6 '18 at 13:58