… ability of Boolean decision trees for the … the decision tree at hand) and on contrastive
explanations (suited to explaining why a given instance has not been classified by the decision tree …

… Before going into the details of decision tree theory, let’s understand why decision trees are
important. Here are the unique advantages of using decision tree algorithms for reference: …

… decision tree algorithms can be taught well in in-service training, but the evaluation of decision
trees is a greater challenge. In our module, we focus on both decision tree algorithms and …

… models, such as decision trees, may be more suitable, as they provide interpretability. Recent
works have proposed the hybridization of decision trees and Reinforcement Learning, to …

… 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 …

… of the decision tree, are known as terminal nodes or decision nodes. In the decision tree
methodology, the classification process is modeled using a set of hierarchical decisions on the …

… 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 …

… , the decision tree can extract readable knowledge rules, which is helpful for university-side
decision-making reference [34,35]. Therefore, this study will use decision trees algorithms, …

… rely on decision trees while making decisions. The construction of decision trees from data
is a longstanding discipline. Statisticians attribute the paternity of regression trees to Sonquist …

… Specifically, we focus on interpretation using decision trees and robustness to l1 … whether
separation implies treebased explanation. We first show that for a decision tree to have …