Explainable Ensemble Trees

• Published in: Computational statistics Vol. 39; no. 1; pp. 3 - 19
• Main Authors: Aria, Massimo, Gnasso, Agostino, Iorio, Carmela, Pandolfo, Giuseppe
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.02.2024; Springer Nature B.V
• Subjects: Algorithms; Classification; Datasets; Decision making; Decision trees; Economic Theory/Quantitative Economics/Mathematical Methods; Machine learning; Mathematics and Statistics; Original Paper; Probability and Statistics in Computer Science; Probability Theory and Stochastic Processes; Statistics; Supervised learning; Random forest; Explainability; Machine learning; Classification
• ISSN: 0943-4062; 1613-9658
• DOI: 10.1007/s00180-022-01312-6

Ensemble methods are supervised learning algorithms that provide highly accurate solutions by training many models. Random forest is probably the most widely used in regression and classification problems. It builds decision trees on different samples and takes their majority vote for classification and average in case of regression. However, such an algorithm suffers from a lack of explainability and thus does not allow users to understand how particular decisions are made. To improve on that, we propose a new way of interpreting an ensemble tree structure. Starting from a random forest model, our approach is able to explain graphically the relationship structure between the response variable and predictors. The proposed method appears to be useful in all real-world cases where model interpretation for predictive purposes is crucial. The proposal is evaluated by means of real data sets.