So far, when discussing classification, we’ve been playing on my toy-dataset (actually, I should no claim it’s mine, it is inspired by the one used in the introduction of Boosting, by Robert Schapire and Yoav Freund). But in ral life, there are more observations, and more explanatory variables.With more than two explanatory variables, it starts to be more complicated to visualise. For instance, consider

MYOCARDE=read.table(
"http://freakonometrics.free.fr/saporta.csv",
head=TRUE,sep=";")

where we have observations from people in E.R., for infarctus, and we want to understand who did survive, to get a predictive model. But before running some classifier, let us visualise our data. Since we have seven explanatory variables and our class (survival or death), we can go for a PCA.

library(FactoMineR) # ACP (sur les var continues)
X=MYOCARDE[,1:7]
acp=PCA(X)

To add the death/survival variable, treat it as numerical 0/1 variable (at least to get a direction)

MYOCARDE2=MYOCARDE
MYOCARDE2$PRONO=(MYOCARDE2$PRONO=="SURVIE")*1
acp=PCA(MYOCARDE2,quanti.sup=8,graph=TRUE)

The nice thing is that we see here where variables are colinear with that one. It is also possible to visualise individuals, and classes, too

acp=PCA(MYOCARDE,quali.sup=8,graph=TRUE)
plot(acp, habillage = 8,col.hab=c("red","blue"))

Continue reading Visualising a Classification in High Dimension →