evtree (version 1.0-2)

BBBClub: Bookbinder's Book Club

Description

Marketing case study about a (fictitious) American book club to whose customers a book about “The Art History of Florence” was advertised.

Usage

data("BBBClub")

Format

A data frame containing 1,300 observations on 11 variables.

Source

Complements to Lilien and Rangaswamy (2004).

Details

The data is a marketing case study about a (fictitious) American book club. taken from the Marketing Engineering textbook of Lilien and Rangaswamy (2004). In this case study, a brochure of the book “The Art History of Florence” was sent to 20,000 customers and 1,806 of which bought the book. A subsample of 1,300 customers is provided in BBBClub for building a predictive model for choice.

The use of a cost matrix is suggested for this dataset. Classifying a customer that purchased the book as a non-buyer is worse (cost = 5), than it is to classify a custumer that did not purchase the book as a buyer (cost = 1).

References

Lilien GL, Rangaswamy A (2004). Marketing Engineering: Computer-Assisted Marketing Analysis and Planning, 2nd edition. Victoria, BC: Trafford Publishing.

Examples

## Not run: 
# ## data, packages, random seed
# data("BBBClub", package = "evtree")
# library("rpart")
# set.seed(1090)
# 
# ## learn trees
# ev <- evtree(choice ~ ., data = BBBClub, minbucket = 10, maxdepth = 2)
# rp <- as.party(rpart(choice ~ ., data = BBBClub, minbucket = 10))
# ct <- ctree(choice ~ ., data = BBBClub, minbucket = 10, mincrit = 0.99)
# 
# ## visualization
# plot(ev)
# plot(rp)
# plot(ct)
# 
# ## accuracy: misclassification rate
# mc <- function(obj) 1 - mean(predict(obj) == BBBClub$choice)
# c("evtree" = mc(ev), "rpart" = mc(rp), "ctree" = mc(ct))
# 
# ## complexity: number of terminal nodes
# c("evtree" = width(ev), "rpart" = width(rp), "ctree" = width(ct))
# 
# ## compare structure of predictions
# ftable(tab <- table(evtree = predict(ev), rpart  = predict(rp),
#   ctree  = predict(ct), observed = BBBClub$choice))
# 
# ## compare customer predictions only (absolute, proportion correct)
# sapply(c("evtree", "rpart", "ctree"), function(nam) {
#   mt <- margin.table(tab, c(match(nam, names(dimnames(tab))), 4))
#   c(abs = as.vector(rowSums(mt))[2],
#     rel = round(100 * prop.table(mt, 1)[2, 2], digits = 3))
# })
# ## End(Not run)