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These accuracies are higher than previously reported results for decision tree.
C4.5 and random forests are the chosen architecture for decision tree implementation.
For decision tree models, we propose a mixed integer programming formulation that is valid for a subclass of RDU models (corresponding to risk seeking behaviors).
For decision tree, scikit-learn provides an optimized version of the classification and regression tree (CART) algorithm [14].
This includes 3D scatter plots, as well as model-dependent views for decision tree or Naive Bayes models.
For decision tree models those built with pruning produced models of higher accuracy than the unpruned trees on the same descriptor set.
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We thus conclude that even a small number of trees are sufficient for decision tree-based models.
With 12 out of 25 studies, the highest number of independently developed models can be found for Markov-models with a similar share of six out of 13 for decision tree-studies.
First, the performance of HMEM with heuristic context clusters is examined; second, the impact of the proposed method for decision tree-based context clustering presented in the Section 3.3 is evaluated.
Such algorithms may not guarantee global optimal solutions for decision trees.
This paper is devoted to the study of bi-criteria optimization problems for decision trees.
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