Tuesday, at the end of my 5-hour crash course on machine learning for actuaries, Pierre asked me an interesting question about computational time of different techniques. I’ve been presenting the philosophy of various algorithm, but I forgot to mention computational time. I wanted to try several classification algorithms on the dataset used to illustrate the techniques
> rm(list=ls()) > myocarde=read.table( "http://freakonometrics.free.fr/myocarde.csv", head=TRUE,sep=";") > levels(myocarde$PRONO)=c("Death","Survival")
But the dataset is rather small, with 71 observations and 7 explanatory variables. So I decided to replicate the observations, and to add some covariates,
> levels(myocarde$PRONO)=c("Death","Survival") > idx=rep(1:nrow(myocarde),each=100) > TPS=matrix(NA,30,10) > myocarde_large=myocarde[idx,] > k=23 > M=data.frame(matrix(rnorm(k* + nrow(myocarde_large)),nrow(myocarde_large),k)) > names(M)=paste("X",1:k,sep="") > myocarde_large=cbind(myocarde_large,M) > dim(myocarde_large)  7100 31 > object.size(myocarde_large) 2049.064 kbytes
The dataset is not big… but at least, it does not take 0.0001 sec. to run a regression. Actually, to run a logistic regression, it takes 0.1 second
> system.time(fit< glm(PRONO~., + data=myocarde_large, family="binomial")) user system elapsed 0.114 0.016 0.134 > object.size(fit) 9,313.600 kbytes
And I was surprised that the regression object was 9Mo, which is more than four times the size of the dataset. With a large dataset, 100 times larger,
> dim(myocarde_large_2)  710000 31
it takes 20 sec.
> system.time(fit<-glm(PRONO~., + data=myocarde_large_2, family="binomial")) utilisateur système écoulé 16.394 2.576 19.819 > object.size(fit) 90,9025.600 kbytes
and the object is ‘only’ ten times bigger.