<ul><li>Recently, there has been a surge of interest in optimal decision tree (ODT) methods that globally optimize accuracy directly.</li><li>A novel extensive experimental study was conducted to provide new insights into the design and behavior of learning decision trees.</li><li>The study identified and analyzed two relatively unexplored aspects of ODTs: the objective function used in training trees and tuning techniques.</li><li>The experimental evaluation examined 11 objective functions, six tuning methods, and six claims from the literature, providing clear recommendations for the usage of greedy and optimal methods.</li></ul>

Optimal or Greedy Decision Trees? Revisiting their Objectives, Tuning, and Performance

Discover more