Local Interpretable Model Agnostic Shap Explanations for machine learning models
This work addresses the need for interpretability in black-box ML models, making predictions more trustworthy for users, though it is incremental as it builds on existing XAI methods.
The authors tackled the problem of interpreting complex machine learning models by proposing LIMASE, a method that combines Shapley values with LIME to provide local and global explanations, achieving faster computation compared to kernel explainers.
With the advancement of technology for artificial intelligence (AI) based solutions and analytics compute engines, machine learning (ML) models are getting more complex day by day. Most of these models are generally used as a black box without user interpretability. Such complex ML models make it more difficult for people to understand or trust their predictions. There are variety of frameworks using explainable AI (XAI) methods to demonstrate explainability and interpretability of ML models to make their predictions more trustworthy. In this manuscript, we propose a methodology that we define as Local Interpretable Model Agnostic Shap Explanations (LIMASE). This proposed ML explanation technique uses Shapley values under the LIME paradigm to achieve the following (a) explain prediction of any model by using a locally faithful and interpretable decision tree model on which the Tree Explainer is used to calculate the shapley values and give visually interpretable explanations. (b) provide visually interpretable global explanations by plotting local explanations of several data points. (c) demonstrate solution for the submodular optimization problem. (d) also bring insight into regional interpretation e) faster computation compared to use of kernel explainer.