For categorical variables, the perturbed data will have binary values of 0 or 1. The way LIME handles categorical variables is different from how it handles the continuous variables. The first 10 lines of the perturbed data are shown in the below illustrations to get an understanding of how the data changes at every stage. Leveraging this business scenario, now we will focus on the interpretation and understanding of how LIME explains predictions. Reference Data-Case 2 - Model comparison & XAI Reference Data-Case 1 - Data treatment & feature selection The complete details of the business case starting from masked raw data, data treatment approach, feature selection methods applied, model comparison, hyperparameter tuning and explainable AI framework, was converted into a solution and has been made available as data cases on our ‘Open Platform’ Analyttica TreasureHunt® LEAPS, below are the links – Complete XAI framework, built on LIME and SHAP techniques, for interpretation of risk score at a customer level
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