Cautious Decision-Making for Tree Ensembles
Cautious classifiers are designed to make indeterminate decisions when the uncertainty on the input data or the model output is too high, so as to reduce the risk of making wrong decisions. In this paper, we propose two cautious decision-making procedures, by aggregating trees providing probability intervals constructed via the imprecise Dirichlet model. The trees are aggregated in the belief functions framework, by maximizing the lower expected discounted utility, so as to achieve a good compromise between model accuracy and determinacy. They can be regarded as generalizations of the two classical aggregation strategies for tree ensembles, i.e., averaging and voting. The efficiency and performance of the proposed procedures are tested on random forests and illustrated on three UCI datasets.
Code (1)
Tasks
Decision MakingSimilar Papers 제목 키워드 기반
TE2Rules: Explaining Tree Ensembles using Rules
Tree Ensemble (TE) models, such as Gradient Boosted Trees, often achieve optimal performance on tabular datasets, yet their lack of transparency poses challenges for comprehending their decision logic. This paper introdu…
Binary ClassificationDecision MakingExplainable artificial intelligenceExplanation GenerationBoosting-Based Sequential Meta-Tree Ensemble Construction for Improved Decision Trees
A decision tree is one of the most popular approaches in machine learning fields. However, it suffers from the problem of overfitting caused by overly deepened trees. Then, a meta-tree is recently proposed. It solves the…
On Computing Optimal Tree Ensembles
Random forests and, more generally, (decision\nobreakdash-)tree ensembles are widely used methods for classification and regression. Recent algorithmic advances allow to compute decision trees that are optimal for variou…
Data-Aware and Scalable Sensitivity Analysis for Decision Tree Ensembles
Decision tree ensembles are widely used in critical domains, making robustness and sensitivity analysis essential to their trustworthiness. We study the feature sensitivity problem, which asks whether an ensemble is sens…
Tree Space Prototypes: Another Look at Making Tree Ensembles Interpretable
Ensembles of decision trees perform well on many problems, but are not interpretable. In contrast to existing approaches in interpretability that focus on explaining relationships between features and predictions, we pro…
Prototype Selection