# Explaining Deep Learning Models for Tabular Data Using Layer-Wise Relevance Propagation
## 1 Introduction
For example, an end user (customer) may question the individual decision a model has taken about them. A regulatory authority may query whether the model is unbiased with respect to gender, ethnicity, and equality. An intermediate user, such as the doctor with the diagnostic scan decision, will want to know what features of the input have resulted in a particular decision. **Scope**: In this paper, we have four main contributions. Firstly, we report recent research work for explaining AI models. We note that there are several comprehensive survey articles on XAI, such as: [PERSON], & [PERSON] [15] discuss explainability in health data, [PERSON] et al. [16] & [PERSON] et al. [17] cover image data for object detection and recognition, and [18] discuss financial data/text data. In addition, detailed surveys on XAI as a field are emerging, such as the detailed and comprehensive survey about explainability, interpretability, and understandability covered in [19]. Secondly, we apply an explainability technique i.e., Layer-wise Relevance Propagation (LRP) for the explanation of a DL model trained over structured/tabular/mixed (in this paper structured, tabular, or mixed is used interchangeably) data as input, in this case a 1-dimensional DL model. Various research works use DL for time series data which is a special case of structured data, where time is a main feature. In our work, we focus on structured data that adheres to a predefined data model in a tabular format but without time features--i.e., non-time series structured data. To the best of our knowledge, this is the first time that LRP has been applied to a model with structured data input. LRP typically uses image as input, providing intuitive visual explanations on the input image. In our work, we train a one dimensional CNN (1D-CNN) model and apply LRP in order to highlight influential features of the input structure data. This approach enables us to answer questions for our selected use case datasets such as: Which factors are causing customers to churn? Why did this specific customer leave? What aspects of this transaction deem it to be classified as fraudulent? There are several other explainability techniques typically used for image-based models e.g., DeepLIFT [20], LEMNA [21], and Grad-CAM [16]. However, although there are several other perturbation approaches e.g., MAPLE [22], LORE [23], and L2X [24] in this work we compare it with two commonly-used XAI techniques in the field: LIME and SHAP. Finally, we validate the correctness of the LRP explanations (important features) by our approach. This is done by taking the most influential subset of features and using them as input for training classifiers in order to see their performance i.e., to determine whether the new models are achieving equal or better performance on the subset of influence features highlighted in the explanation (testing set) compared to the models trained over the whole set of features. The paper is organised as follows: Section 2 gives an overview of related work. Section 3 explains the proposed approach that includes the datasets used, pre-processing performed, models trained, and finally model explanation details. Then, Section 4 discusses the results achieved with the proposed approach, highlighting important features, as well as, results with the subset of features. Finally, Section 5 gives some future directions and main conclusions of our paper.