Add new interpretability techniques

[afraniomelo]

How we are today

To improve interaction and adoption by users and operators at process industries, it is important that models are interpretable. At present, the interpretability functionalities provided by BibMon are limited to the sklearnRegressor class and rely solely on feature importances.

Proposed enhancement

We propose the implementation of advanced interpretability techniques such as LIME (local interpretable model-agnostic explanations) (Ribeiro et al., 2016) and SHAP (Shapley additive explanations) (Lundberg and Lee, 2017).

Implementation

Ideally, these functionalities should be implemented in files such as _generic_model.py or _bibmon_tools.py. This approach will ensure that the new interpretability techniques are accessible for all models within the library.