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I was looking at an example from Predictive Modeling Applications in Actuarial Science

And, in one of the examples (below) -- he moves cars with 6, 7, 8 and 9 seats into the categorical variable 6+ -- why does he do that?

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# Roll high values of seats (categorical variable) into a lower value
idx <- dta$seats %in% c("6", "7", "8", "9")
dta$seats[idx] <- "6+"
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This is a part of the feature engineering stage in modeling.
Two key concepts in features engineering are:
1) Features should represent reality(or our assumption about reality).
2) Features different values should have good distribution(where good might mean different things).

Bucketing over 6 seats cars into one value lets one keep its reality representation while improving its feature values distribution.

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