Employing LIME
Until now, the model-agnostic interpretation methods we’ve covered attempt to reconcile the totality of outputs of a model with its inputs. For these methods to get a good idea of how and why X becomes y_pred, we need some data first. Then, we perform simulations with this data, pushing variations of it into a model and evaluating what comes out of the model. Sometimes, they even leverage a global surrogate to connect the dots. By using what we learned in this process, we yield feature importance values that quantify a feature’s impact, interactions, or decisions on a global level. For many methods such as SHAP, these can be observed locally too. However, even when they can be observed locally, what was quantified globally may not apply locally. For this reason, there should be another approach that quantifies the local effects of features solely for local interpretation—one such as LIME!
What is LIME?
LIME trains local surrogates to explain...
