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Random forest explainability using counterfactual sets
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DOI：10.1016/j.inffus.2020.07.001.png)
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Abstract
En 中文
Nowadays, Machine Learning (ML) models are becoming ubiquitous in today's society, supporting people with their day-to-day decisions. In this context, Explainable ML is a field of Artificial Intelligence (AI) that focuses on making predictive models and their decisions interpretable by humans, enabling people to trust predictive models and to understand the underlying processes. A counterfactual is an effective type of Explainable ML technique that explains predictions by describing the changes needed in a sample to flip the outcome of the prediction. In this paper, we introduce counterfactual sets, an explanation approach that uses a set of counterfactuals to explain a prediction rather than a single counterfactual, by defining a sub-region of the feature space where the counterfactual holds. A method to extract counterfactual sets from a Random Forest (RF), the Random Forest Optimal Counterfactual Set Extractor (RF - OCSE), is presented. The method is based on a partial fusion of tree predictors from a RF into a single Decision Tree (DT) using a modification of the CART algorithm, and it obtains a counterfactual set that contains the optimal counterfactual. The proposal is validated through several experiments against existing alternatives on ten well-known datasets by comparing the percentage of valid counterfactuals, distance to the factual sample, and counterfactual sets quality.
Keywords:
Explainable machine learning
Counterfactual sets
Counterfactual
Information fusion
Random forest
Decision tree
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