dat <- data.frame(y = runif(1000), x=rnorm(1000))

gbmMod <- gbm::gbm(y~x, data=dat, n.trees=5000, cv.folds=0)

summary(lm(predict(gbmMod, n.trees=5000) ~ dat$y))$adj.r.squared

inds <- sample(1:nrow(dat), 0.7*nrow(dat))

train <- dat[inds, ]
test <- dat[-inds, ]

gbmMod2 <- gbm::gbm(y~x, data=train, n.trees=5000)

preds <- predict(gbmMod2, newdata = test, n.trees=5000)

summary(lm(preds ~ test[,1]))$adj.r.squared