# Specific-Input LIME Explanations for Tabular Data Based on Deep Learning Models
## 2 Literature Review of Explainability
### The Scope of Interpretability  
Interpretability refers to the ability to understand an automated model. There are two types of interpretability: understanding the complete behaviour of the model or understanding a specific prediction. In the literature reviewed, contributions are made in both directions. Consequently, interpretability is divided into two categories: (i) global interpretability and (ii) local interpretability.  
When deep learning models are needed to inform population-level decisions, such as drug consumption trends or climate change, this family of approaches comes in handy. In such circumstances, a global effect estimate might be more valuable than a long list of possible reasons. The pneumonia risk prediction model and the rule set built from a sparse Bayesian generative model proposed in Section 2.2 are examples of work that provides globally interpretable models.  
[PERSON] et al. proposed using recursive partitioning to generate a global interpretation tree for a wide range of ML models based on their local explanations [10]. In their experiments, the authors showed that their approach can detect whether a particular machine learning model is working sensibly or being overly adapted to an illogical pattern.  
While various methods have been used in the literature to promote global interpretability, global models can be challenging in practice, especially for models with more than a few parameters. Local interpretability can be more easily applied, similar to human efforts to understand the whole model by focusing on only part of it.  
Explainability occurs locally when explaining the reasoning behind a particular prediction. This aspect of interpretability is used to generate a unique explanation of why the model makes a particular decision in a particular situation. Several studies have proposed local explanatory approaches. The following section summarises the explanatory approaches examined in the peer-reviewed studies.  
LIME stands for local interpretable model-agnostic explanations, as presented by [PERSON] et al. [11]. This model can approximate a black-box model near any prediction.  
[PERSON] et al. make another attempt to generate local explanations. The authors provide a method that uses local gradients to specify how a data point must move to change its predicted label, in order to explain the local decisions made by arbitrary nonlinear classification algorithms [12]. In this vein, we find a number of studies of image classification models that use a similar strategy. Locating regions of an image that have a strong influence on the final classification is a typical technique for understanding the decisions of image classification algorithms.  
Combining the strengths and benefits of both local and global interpretability is an exciting and promising research path. The following are the four possible combinations: (i) How does the model create predictions? (ii) On a modular level, global model interpretability identifies how different elements of the model influence predictions. (iii) For a group of predictions, local interpretability reveals why the model made specific conclusionsfor a set of cases. (iv) Finally, the model's usual local interpretability for a single prediction is utilised to demonstrate why it drew a given conclusion for a particular case [13].  
Another point worth mentioning is that local explanations are the most common way of generating explanations in DNNs in the literature reviewed. Although these methods were developed to explain neural networks, authors often emphasise that they can be used to explain any type of model, making them agnostic models.