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DiCE: Diverse Counterfactual Explanations for Hotel Cancellations
DiCE is part of the broader InterpretML library, and is great at generating “Diverse Counterfactual Explanations” for a particular dataset.
By “Diverse Counterfactual Explanations”, we mean decisions made by a model that would be influential enough to change the outcome variable.
Let’s take an example. Suppose that a hotel manager is analysing customer bookings, and wishes to analyse which types of customers are more likely to cancel their booking.
Here is an instance where a customer did not cancel their hotel booking.
According to this particular case, the customer belongs to the market segment Offline TA/TO, the customer type is Contract, the Distribution Channel is TA/TO and Required Car Parking Spaces is 0.
However, a hotel manager who is looking to minimise cancellation bookings would ideally like to identify the types of customers who would be likely to cancel their bookings. This is where DiCE comes in.
When looking at the counterfactual explanations above — instances where a customer did cancel their bookings, it is observed that:
- Lead Time is significantly higher across the four…