… We consider the use of decision trees for decision-making problems under the … a decision
tree to predict unknown input parameters of an optimization problem, and then make decisions …

… Alternatively, we can gain insight into the model’s decision … decisions as in rule-based
models like decision trees. However, existing efforts to fuse deep learning and decision trees …

… a general framework for decision tree optimization that addresses … We present techniques
that produce optimal decision trees … ables and speeds up decision tree construction by several …

… the problem of extending a decision tree from available data, … of Decision tree classifiers
has been proposed in many ways. This paper provides a detailed approach to the decision trees…

… green (yes) arrows route the decision. Each vertical chain in this decision thicket is a decision
tree. We marked two such decision trees as tree 1 and tree 2 with yellow and green boxes, …

… decision trees, splitting criteria for selecting best attribute and pruning methods. The readers
will be able to understand why decision trees … about a decision tree induction algorithms, …

… decision tree. Third, we conduct a user study to quantify the interpretability of a decision tree,
… In this paper, we advance the state of the art in decision tree methods for RL and leverage …

… It owes its name to that of a tree because of its similarity in shape. The root node is a starting
… making it a tree-like structure. Decision tree simply forks the tree into sub-trees on the basis …

… In this study, the best first decision tree was tested as a standalone model and was compared
to the effectiveness of bagging and dagging ensemble models. Twelve conditioning factors …

… forests, and in particular Gradient Boosting Decision Trees (… a decision forest of any kind into
an interpretable decision tree… exploit the interpretability of decision trees without significantly …