# TODO: Add comment # # Author: mike-bowles ############################################################################### rm(list=ls()) require(gbm) str(iris) Species <- rep(0,150) Species[1:50] <- 1 iris$Species <- Species Xiris <- iris[,1:4] gbm1 <- gbm(Species~., data=iris, shrinkage=0.005, interaction.depth=3, distribution="bernoulli", cv.folds=5, n.trees=2000) best.iter <- gbm.perf(gbm1,method="cv") predict <- predict(gbm1,Xiris,best.iter) predict Species <- rep(0,150) Species[51:100] <- 1 iris$Species <- Species Xiris <- iris[,1:4] gbm1 <- gbm(Species~., data=iris, shrinkage=0.005, interaction.depth=3, distribution="bernoulli", cv.folds=5, n.trees=2000) best.iter <- gbm.perf(gbm1,method="cv") predict <- predict(gbm1,Xiris,best.iter) predict Species <- rep(0,150) Species[101:150] <- 1 iris$Species <- Species Xiris <- iris[,1:4] gbm1 <- gbm(Species~., data=iris, shrinkage=0.005, interaction.depth=3, distribution="bernoulli", cv.folds=5, n.trees=2000) best.iter <- gbm.perf(gbm1,method="cv") predict <- predict(gbm1,Xiris,best.iter) predict