… decision tree algorithms are often greedy or myopic, and sometimes produce unquestionably
suboptimal models. Hardness of decision tree … for optimal decision trees for binary variables…

… 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 …

… 1, given a pre-trained CNN, we propose a method to construct a decision tree to explain CNN
… all images into various decision modes. Each tree node represents a decision mode. Each …

… Decision trees (DTs) are such a tool. Their goal is consisted … The most commonly used
applications of decision trees are … Decision trees can also handle complicated relations by …

… decision tree algorithms which includes ID3, C4.5, C5.0, and CART (Classification and
Regression Trees) … In this paper, the prediction performance of decision tree algorithms will be …

… of these MIP based models to general decision-making tasks in socially sensitive settings …
trees introduced in (Verwer and Zhang 2017), we consider more flexible decision tree models …

… 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…

… decision tree-based machine learning algorithms through the lens of adversarial examples.
We study both classical decision trees … We show that, similar to neural networks, tree-based …

… To exploit the similarity information between the instances from different parties, we propose
a new approach to build the decision tree, which is called Weighted Gradient Boosting (…