Explainable Optimal Random Forest model with conversational interface

Caroline Mary M.

doi:10.1016/j.engappai.2025.110134

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Explainable Optimal Random Forest model with conversational interface

2025-04-01

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Caroline Mary M. *

H. S. Jennath

S. Asharaf

DOI：10.1016/j.engappai.2025.110134

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Abstract

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Machine Learning (ML) models are now apart of our everyday lives and significantly impact each individual. When users are given a clear and relevant explanation for a decision made by the model, they can understand the reasons and logic behind the model's behavior, which helps to build trust in the model. This article proposes a zeroth level of explainability for an Optimal Random Forest model, which incorporates an Optimal Sparse Decision tree that is self-explainable. The methodology also involves vectorizing subtrees relevant to an instance, followed by dimensionality reduction and clustering. This method enhances explainability, ensuring high fidelity in the explanations provided. It also proposes a conversational interface for explaining the model's decisions. Experimental analysis was done on four benchmark datasets, namely SMS Spam collection dataset (classification problem of ham or spam messages), Iris dataset (classification problem of Setosa or Non-Setosa based on length and width information of both sepal and petal data), Tic-tac-toe endgame dataset (based onboard position content and their corresponding class labels), Heart Disease dataset (classification problem of prone to heart disease or not). Experimental analysis has demonstrated the explainability of the Optimal Random Forest model without any compromise on generalization and performance. Comparisons were also made with Shapley Additive Explanation (SHAP) derived from both a generalized random forest model and a Support Vector Machine model, and also on Rule learning + Voting model. The results have demonstrated the merits of the algorithm.

Keywords:

Explainability

Explainable Artificial Intelligence

Optimal Random Forest