Guided-LIME: Structured Sampling based Hybrid Approach towards Explaining Blackbox Machine Learning Models

Many approaches to explain machine learning models and interpret its results have been proposed. These include shadow model approaches, like LIME and SHAP; model inspection approaches like Grad-CAM and data-based approaches like Formal Concept Analysis (FCA). Explanations of the decisions of blackbox ML models using any one of these approaches has their limitations as the underlying model is rather complex. Running explanation model for each sample is not cost-efficient. This motivates to design a hybrid approach for evaluating interpretability of blackbox ML models. One of the major limitations of widely-used LIME explanation framework is the sampling criteria that is employed in SP-LIME algorithm for generating a global explanation of the model. In this work, we investigate a hybrid approach based on LIME using FCA for structured sampling of instances. The approach combines the benefits of using a data-based approach (FCA) and proxy model-based approach (LIME). We evaluate these models on three real-world datasets: IRIS, Heart Disease and Adult Earning dataset. We evaluate our approach based on two parameters: 1) by measuring the prominent features in the explanations, and 2) proximity of the proxy model to the original blackbox ML model. We use calibration error metric in order to measure the closeness between blackbox ML model and proxy model.