rdigitsBFOS: BFOS Digit Recognition Problem
In gencve: General Cross Validation Engine

Description Usage Arguments Details Value Note Author(s) References See Also Examples

BFOS suggested this is data generation model for testing the performance of nonlinear classifiers such as CART. See details and vignette.

1	rdigitsBFOS(n, eta = 0.25, alpha = NULL, silent = FALSE)

`n`	Number of 10-tuples to generated.
`eta`	Bayes optimal missclassification rate.
`alpha`	Default is Null but if specified it is theprobability line segment is flipped. When alpha is specified corresponding the Bayes rate is determined and shown.
`silent`	Default is FALSE and in this case the title is displayed otherwise no display.

Breiman et al. (1984, Section 2.6.1, p.43) mentioned the case alpha=0.1 and stated that the Bayes optimal rule has a 0.26 mis-classification rate. Derivation of this and more details are discussed in the vignette.

A dataframe with 10*n rows and 8 columns is produced. Columns 1 to 7 are labeled x1, ..., x7 and correspond to the inputs which are the line segments comprising each digit where 1 indicates on and 0 off. Column 8 is a factor with value the digit, 0, 1, ..., 9. Each successive block of ten rows corresponds to ten successive digits.

An attribute "title" is created.

A. I. McLeod

BFOS (Breiman, Friedman, Olshen, and Stone), 1984 Classification and Regression Trees

rxor, rmix, ShaoReg

#debug-rdigitsBFOS.R
#with alpha=0.1, not significantly different from 0.25
require("C50")
n <- 1000
Xy <- rdigitsBFOS(n, alpha=0.1)
attr(Xy, "title")
names(Xy)
ans <- C5.0(digit~., data=Xy)
XyTest <- rdigitsBFOS(n, alpha=0.1)
yHat <- predict(ans, newdata=XyTest[,1:7])
eta <- mean(yHat!=XyTest$digit)
MOE95pc <- 1.96*sqrt(eta*(1-eta)/(10*n))
round(100*unlist(list(misclassifcationRate=eta, "95pcMOE"=MOE95pc)),1)