Demo of how to use grid search on H2O's XGBoost: http://docs.h2o.ai/h2o/latest-stable/h2o-docs/data-science/xgboost.html

h2o_xgboost_grid_example.R

library(h2o)
h2o.init()


# Load the HIGGS dataset
train <- h2o.importFile("https://s3.amazonaws.com/erin-data/higgs/higgs_train_10k.csv")
test <- h2o.importFile("https://s3.amazonaws.com/erin-data/higgs/higgs_test_5k.csv")
y <- "response"
x <- setdiff(names(train), y)
family <- "binomial"

#For binary classification, response should be a factor
train[,y] <- as.factor(train[,y])
test[,y] <- as.factor(test[,y])

# Some XGboost/GBM hyperparameters
hyper_params <- list(ntrees = seq(10, 1000, 1),
                     learn_rate = seq(0.0001, 0.2, 0.0001),
                     max_depth = seq(1, 20, 1),
                     sample_rate = seq(0.5, 1.0, 0.0001),
                     col_sample_rate = seq(0.2, 1.0, 0.0001))
search_criteria <- list(strategy = "RandomDiscrete",
                        max_models = 10, 
                        seed = 1)

# Train the grid
xgb_grid <- h2o.grid(algorithm = "xgboost",
                     x = x, y = y,
                     training_frame = train,
                     nfolds = 5,
                     seed = 1,
                     hyper_params = hyper_params,
                     search_criteria = search_criteria)


# Sort the grid by CV AUC
grid <- h2o.getGrid(grid_id = xgb_grid@grid_id, sort_by = "AUC", decreasing = TRUE)
grid_top_model <- grid@summary_table[1, "model_ids"]

Helo,
does not work under Win7 12 core. Tried H2o 3.16 and 3.17. Any hint available?

Hi Steviey, xgboost from H2o is not available for windows as of now. check the limitations section below

http://docs.h2o.ai/h2o/latest-stable/h2o-docs/data-science/xgboost.html

I am also in the same boat of yours, I did 2 things, one is ubuntu via virtualbox and the other one is ubuntu machine on aws. This works over there. Hope this helps.

Thanks rknimmakayala,
thats's a little bit to much for me. I do it native in r via caret grid search. Works like a charme.

set.seed(42)
        # xgboost train as.matrix + grid.search...
        # https://www.r-bloggers.com/r-setup-a-grid-search-for-xgboost/
        bootControl <- trainControl(number=4, verboseIter=TRUE)

        # set up the cross-validated hyper-parameter search
        xgb_grid_1 = expand.grid(
            eta = c(0.01, 0.001, 0.0001),
            max_depth = c(2, 4, 6, 8, 10, 15, 45),
            gamma = 1,
            nrounds = 1400,
            colsample_bytree = 1,    #default=1
            min_child_weight = 1,     #default=1
            subsample = 1
        )
        
        # pack the training control parameters
        xgb_trcontrol_1 = trainControl(
            method = "repeatedCV",
            number = 15,
            verboseIter = TRUE,
            returnData = FALSE,
            returnResamp = "all",# save losses across all models
            #classProbs = TRUE,# set to TRUE for AUC to be computed
            summaryFunction = twoClassSummary,
            allowParallel = TRUE,
            savePred="final",
            repeats=5
        )
    
        # train the model for each parameter combination in the grid,
        modFit = train(
            #x = as.matrix(f),
            #y = as.factor(y),
            #x = as.matrix(f),
            #y = as.factor(y),
            f, 
            data=training,
            #data=as.matrix(training),
            trControl = xgb_trcontrol_1,
            tuneGrid = xgb_grid_1,
            method = "xgbTree",
            metric=metric
        )