Cross Validation to Determine Error of Embedding Alg composed with Classifier
require(lolR)require(ggplot2)n =400d =30r =3Here, we look at how to see the generalizability of a given model in the form of the cross-validated error. We simulate data withn=200 andd=30:
testdat <-lol.sims.rtrunk(n, d)X <-testdat$XY <-testdat$Ydata <-data.frame(x1=X[,1],x2=X[,2],y=Y)data$y <-factor(data$y)ggplot(data,aes(x=x1,y=x2,color=y))+geom_point()+xlab("x1")+ylab("x2")+ggtitle("Simulated Data")We arbitrarily select LOL as our algorithm, and look at the leave-one-out (loo) cross-validated error with the LDA classifier. We project the resulting model to3 dimensions and visualize the first2:
result <-lol.xval.eval(X, Y, r,alg = lol.project.lol,alg.return="A",classifier=MASS::lda,classifier.return="class",k='loo')data <-data.frame(x1=result$model$Xr[,1],x2=result$model$Xr[,2],y=Y)data$y <-factor(data$y)ggplot(data,aes(x=x1,y=x2,color=y))+geom_point()+xlab("x1")+ylab("x2")+ggtitle(sprintf("Projected Data using LOL, L=%.2f", result$lhat))