  This paper discusses a data-driven, empirically-based framework to make
algorithmic decisions or recommendations without expert knowledge. We improve
the performance of two algorithmic case studies: the selection of a pivot rule
for the Simplex method and the selection of an all-pair shortest paths
algorithm. We train machine learning methods to select the optimal algorithm
for given data without human expert opinion. We use two types of techniques,
neural networks and boosted decision trees. We concluded, based on our
experiments, that:
  1) Our selection framework recommends various pivot rules that improve
overall total performance over just using a fixed default pivot rule.
  Over many years experts identified steepest-edge pivot rule as a favorite
pivot rule. Our data analysis corroborates that the number of iterations by
steepest-edge is no more than 4 percent more than the optimal selection which
corroborates human expert knowledge, but this time the knowledge was obtained
using machine learning. Here our recommendation system is best when using
gradient boosted trees.
  2) For the all-pairs shortest path problem, the models trained made a large
improvement and our selection is on average .07 percent away from the optimal
choice. The conclusions do not seem to be affected by the machine learning
method we used.
  We tried to make a parallel analysis of both algorithmic problems, but it is
clear that there are intrinsic differences. For example, in the all-pairs
shortest path problem the graph density is a reasonable predictor, but there is
no analogous single parameter for decisions in the Simplex method.
