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Visual Exploration of Machine Learning Model Behavior with Hierarchical Surrogate Rule Sets
[article]
2022
One of the potential solutions for model interpretation is to train a surrogate model: a more transparent model that approximates the behavior of the model to be explained. Typically, classification rules or decision trees are used due to the intelligibility of their logic-based expressions. However, decision trees can grow too deep and rule sets can become too large to approximate a complex model. Unlike paths on a decision tree that must share ancestor nodes (conditions), rules are more
doi:10.48550/arxiv.2201.07724
fatcat:kr5duuxjynea7fbryhspsnx5q4