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...ot;poisson", n.cores=2) range(gbmpred1$Predictions) [1] 10.04643 31.39230 # the expected predictions # Here are results from xgboost # use count:poisson library(xgboost) xgbst2.1 <- xgboost(data = as.matrix(sponge[, -c(3)]), label = sponge[, 3], max_depth = 2, eta = 0.001, nthread = 6, nrounds = 3000, objective = "count:poisson") xgbstpred2 <- predict(xgbst2.1, as.matrix(sponge.grid)) head(xgbstpred2) range(xgbstpred2) [1] 1.109032 4.083049 # much lower than expected table(xgbstpred2) 1.10903215408325 1.26556181907654 3.578040599823 4.08304929733276...

...his video https://www.youtube.com/watch?v=z8PRU46I3NY uses the titanic data as an example of using caret to create xgbTree models. The caret train() function has a tuneGrid parameter which takes a list set up like so: tune.grid <- expand.grid(eta = c(0.05, 0.075, 0.1), nrounds = c(50, 75, 100), max_depth = 6:8, min_child_weight = c(2, 2.25, 2.5), colsample_bytree = (3:5)/10, gamma = 0, subsample = 1) That approach also worked with my data. By making the corresponding adj...