# Specific-Input LIME Explanations for Tabular Data Based on Deep Learning Models
## 5 Conclusions  
We use the specific-input LIME method to provide the user with a more detailed explanation. Using our method, it is possible to obtain the influence of the data points that need to be explained within a particular interval, and this influence provides the user with a more intuitive explanation.  
This approach can also have many applications in practice, such as in healthcare, where this combination of global and locally interpretable models can be used to explain how machine learning models predict whether a patient has a certain disease or not, and to analyse the impact of individual features on this prediction result. This can help physicians and patients to better understand the prediction results and help them make better decisions.  
Although our method gives good results, it still has limitations. Since LIME, PDP, and feature importance are generated based on the perturbed data predicted by the model, they may be sensitive to the size and direction of the perturbations. If the perturbation is too large or in the wrong direction, it may lead to an unstable interpretation, so we will focus on this issue in future work. The perturbation data require the generation of a large amount of perturbation data, which can lead to high computational costs, especially for complex models or large datasets.  
In future work, we will focus on parameter optimisation to reduce the computational cost and improve the robustness of this method.  
Conceptualization, J.A.; methodology, J.A.; software, J.A., Y.Z.; validation, J.A., Y.Z.; formal analysis, J.A., Y.Z.; investigation, J.A., Y.Z.; resources, I.J.; data curation, J.A., Y.Z.; writing--original draft preparation, J.A.; writing--review and editing, Y.Z., I.J.; visualization, J.A., Y.Z.; supervision, I.J.; project administration, I.J.; funding acquisition, I.J. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Institute of Information & Communications Technology Planning & Evaluation (IITP) grant funded by the Korea government (MSIT) (No. 2020-0-00107, development of technology to automate recommendations for big data analytic models that define data characteristics and problems).  
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.  
This data can be found here: [[https://www.kaggle.com/datasets/ucml/red-wine-quality-cortez-et-al-2009](https://www.kaggle.com/datasets/ucml/red-wine-quality-cortez-et-al-2009)]([https://www.kaggle.com/datasets/ucml/red-wine-quality-cortez-et-al-2009](https://www.kaggle.com/datasets/ucml/red-wine-quality-cortez-et-al-2009)).  
The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.  
As there are many abbreviations of technical terms in this paper, the following appendix explains these abbreviations:  
LIMELocal interpretable model-agnostic explanations  
XAIExplainable artificial intelligence  
PDFPartial dependence plots  
AIArtificial intelligence  
DNNsDeep neural networks  
BRLBayesian rule lists  
AUCArea under curve  
ROCReceiver operating characteristic curve  
SHAPShapley additive explanation