On Explaining Decision TreesYacine IzzaAlexey IgnatievJoo Marques-Silva
Learning Optimal and Fair Decision Trees for Non-Discriminative Decision-Making (AAAI 2019) Sina Aghaei, Mohammad Javad Azizi, Phebe Vayanos [Paper] Desiderata for Interpretability: Explaining Decision Tree Predictions with Counterfactuals (AAAI 2019) Kacper Sokol, Peter A. Flach [Paper] Weighted Obliq...
Although deep reinforcement learning has become a promising machine learning approach for sequential decision-making problems, it is still not mature enoug
the principles related to explainability gained strength from the 1970 s onwards. The earliest works presented rule-based structures and decision trees with human-oriented explanations
Along the northern border, the renewed fear of border-breaching has created one of the weirdest manifestations of a surveillance state to our northern borders, with the clearing of trees on the US-Canada border, known locally and colloquially as the “Border Slash.” US-Canada Border Slash/...
HOLOP Bandit-Based Planning and Learning inContinuous-Action Markov Decision Processes, Weinstein A., Littman M. (2012). BRUE Simple Regret Optimization in Online Planning for Markov Decision Processes, Feldman Z. and Domshlak C. (2014). LGP Logic-Geometric Programming: An Optimization-Based Appr...
(alpha = 0.5) were performed as linear regression methods using the R package glmnet ver. 4.062. Random Forest, a non-linear decision tree-based ensemble learning method, was performed using the R package randomForest ver. 4.6-1463. Number of trees (ntree) was set to 1000, and ...
the output for each instance by averaging the predictions of each of these trees. By usingrandomizationof samples and averaging results from multiple trees—an approach that is also known as theensemble method—bagging will address some of the overfitting that results from a single decision tree. ...
Nevertheless, sometimes a combination of unsupervised (e.g., clustering algorithms) and supervised algorithms (e.g., decision trees) can enable interpretability. Pitombo et al., 2011 used this modeling approach to analyze the pattern-travel relationship involving activity, land use, and socioeconomic...
Our main aim was not to outperform previous tissue classifiers; however, we show that our CNN model obtained high performance scores on held-out GTEx samples and an independent dataset, thus explaining the decision driving features of such a model was worthwhile. The most poorly predicted tissues...