Abstract
In recent years, black-box models have developed rapidly because of their high accuracy. Balancing the interpretability and accuracy is increasingly important. The lack of interpretability severely limits the application of the model in academia and industry. Despite the various interpretable machine learning methods, the perspective and meaning of the interpretation are also different. We provide a review of the current interpretable methods and divide them based on the model being applied. We divide them into two categories: interpretable methods with the self-explanatory model and interpretable methods with external co-explanation. And the interpretable methods with external co-explanation are further divided into subbranch methods based on instances, SHAP, knowledge graph, deep learning, and clustering model. The classification aims to help us understand the model characteristics applied in the interpretable method better. This survey makes the researcher find a suitable model to solve interpretability problems more easily. And the comparison experiments contribute to discovering complementary features from different methods. At the same time, we explore the future challenges and trends of interpretable machine learning to promote the development of interpretable machine learning.
Highlights
With the rapid development of Neural Network (NN) and Deep Learning (DL), such NN models trained by some complex processes are like a black box and it is hard to understand why it works so well
The interpretable methods with external co-explanation are divided from the following three dimensions, which mainly includes the interpretation of the black-box model with data examples, the interpretation of the black-box model with a specific method, and the interpretation of the black-box model itself
Sun et al presented a method named recurrent knowledge graph embedding (RKGE) [53], which adopts a recurrent network architecture to automatically learn the semantic representation of the path between entities
Summary
The interpretable machine learning method assists us to solve problems It gives evidence of the black-box model in applications. The interpretable methods with external co-explanation are divided from the following three dimensions, which mainly includes the interpretation of the black-box model with data examples, the interpretation of the black-box model with a specific method (this paper takes SHAP and KG as examples), and the interpretation of the black-box model itself (this paper takes deep learning and clustering model as examples) The advantage of this division is that it highlights the characteristics of different fields and finds better solutions to the problems in machine learning. Discussions of future works and challenges are presented
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