Explaining black-box models using attribute importance, PDPs, and LIME
Domino Data Lab
AUGUST 1, 2021
After forming the X and y variables, we split the data into training and test sets. Looking at the target vector in the training subset, we notice that our training data is highly imbalanced. All we need to do is instantiate LimeTabularExplainer and give it access to the training data and the independent feature names.
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