DLIME uses agglomerative hierarchical clustering and KNN to generate stable local explanations for black-box ML predictions on medical data, outperforming LIME on Jaccard similarity of repeated explanations.
Locally Interpretable Models and Effects based on Supervised Partitioning (LIME-SUP)
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abstract
Supervised Machine Learning (SML) algorithms such as Gradient Boosting, Random Forest, and Neural Networks have become popular in recent years due to their increased predictive performance over traditional statistical methods. This is especially true with large data sets (millions or more observations and hundreds to thousands of predictors). However, the complexity of the SML models makes them opaque and hard to interpret without additional tools. There has been a lot of interest recently in developing global and local diagnostics for interpreting and explaining SML models. In this paper, we propose locally interpretable models and effects based on supervised partitioning (trees) referred to as LIME-SUP. This is in contrast with the KLIME approach that is based on clustering the predictor space. We describe LIME-SUP based on fitting trees to the fitted response (LIM-SUP-R) as well as the derivatives of the fitted response (LIME-SUP-D). We compare the results with KLIME and describe its advantages using simulation and real data.
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cs.LG 1years
2019 1verdicts
UNVERDICTED 1representative citing papers
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DLIME: A Deterministic Local Interpretable Model-Agnostic Explanations Approach for Computer-Aided Diagnosis Systems
DLIME uses agglomerative hierarchical clustering and KNN to generate stable local explanations for black-box ML predictions on medical data, outperforming LIME on Jaccard similarity of repeated explanations.