An experimental AutoML module
In this section, we will implement ML models in the spirit of LIME. We will play by the rules and try not to influence the outcome of the ML models, whether we like it or not.
The LIME explainer will try to explain predictions no matter which model produces the output or how.
Each model 
will be treated equally as part of 
, our set of models:
We will implement five machine learning models with their default parameters, as provided by scikit-learn's example code.
We will then run all five machine learning models in a row and select the best one with an agnostic scoring system to make predictions for the LIME explainer.
Each model will be created with the same template and scoring method.
This experimental model will only choose the best model. If you wish to add features to this experiment, you can run epochs. You can develop functions that will change the parameters of the module during...
