  Automated algorithm selection and configuration methods that build on
exploratory landscape analysis (ELA) are becoming very popular in Evolutionary
Computation. However, despite a significantly growing number of applications,
the underlying machine learning models are often chosen in an ad-hoc manner.
  We show in this work that three classical regression methods are able to
achieve meaningful results for ELA-based algorithm selection. For those three
models -- random forests, decision trees, and bagging decision trees -- the
quality of the regression models is highly impacted by the chosen
hyper-parameters. This has significant effects also on the quality of the
algorithm selectors that are built on top of these regressions.
  By comparing a total number of 30 different models, each coupled with 2
complementary regression strategies, we derive guidelines for the tuning of the
regression models and provide general recommendations for a more systematic use
of classical machine learning models in landscape-aware algorithm selection. We
point out that a choice of the machine learning model merits to be carefully
undertaken and further investigated.
