## 2 Related work
### Explainable Artificial Intelligence  
The machine learning models are growing in complexity and sophistication providing accurate forecasts based on historic data. At the same time, there is an increasing need to understand the logic behind such models, to comply with regulatory requirements, and provide ground for responsible decision-making [40; 41]. Insights on the process followed by such models when applying operations on the input to provide a forecast enable a decision about whether such forecasts can be trusted or not [42; 43]. To respond to such challenges, research on techniques, approaches and visualizations is done in a sub-field of artificial intelligence, known as Explainable Artificial Intelligence (XAI). Multiple taxonomies were proposed to categorize the different XAI approaches. [PERSON] et al. distinguish between transparent models and post-hoc explainability techniques, dividing the last category into model-agnostic and model-specific approaches [44]. Transparent models are also known as inherently interpretable or _white-box models_, while the models that do not fall into this category, are considered opaque or _black-box models_[45]. A more elaborate taxonomy was proposed by [PERSON] et al. [46], who considered dividing XAI techniques based on three criteria: scope (considering global or local explanations), methodology (if the technique focuses on the input data or model parameters), and usage (if is model-agnostic or model-specific). Regarding the scope, local explanations provide insights regarding a particular forecast, while global explanations attempt to describe the overall model behavior [47]. When providing global explanations for models trained on tabular data, a frequent model-specific approach is to consider the feature weight in the model to determine the feature relevance ranking. Model-agnostic alternatives have been devised by several authors using surrogate models [48; 49; 50]. Although much research has been done on explaining model behavior, less research was invested towards crafting comprehensive explanations with insights regarding the data and the model creation process. Part of this void was addressed by MELODY (MachinE-Learning MODel SummarY) [51], and SUBPLEX [52], which connect local explanations to data analytics either summarizing insights regarding the whole dataset or a relevant subpopulation. INFUSE [53], on the other side, focused on providing explanations regarding the feature selection process, and the influence of different feature selection strategies on it. Although INFUSE takes into account a process of cross-validation, it does not bind it to the resulting model and any model related explanations. Visual interpretations are considered particularly effective to explain the model forecasting rationale [54]. Although much work was invested towards developing XAI techniques, some researchers consider not enough research was invested on making such explanations end-user-centered [55; 56]. Visual explanations comprehend insights regarding the dataset and feature contributions at a local and global level. Scatterplots are frequently used to visualize data distribution, using some dimensionality reduction technique to map the high-dimensional dataset into two dimensions [57; 58]. Color-coded instances are frequently used in classification tasks, and interactive interfaces provided, to enable the user focus on specific instances and conduct further research [47]. To represent feature contributions, horizontal bar plots [52; 59; 60], breakdown plots [61; 62], heatmaps [63; 64], Partial Dependence Plots [65], or Accumulated Local Effects Plots [66] are used. In this research, we complemented our model development with a dashboard that provides insights into the most informative features within the dataset, when considering feature selection, while also informing their relevance from the model point of view. In addition, we inform the value ranges of each sensor's readings found in the dataset.