Builds a eXtreme Gradient Boosting model using the native XGBoost backend.

```
h2o.xgboost(
x,
y,
training_frame,
model_id = NULL,
validation_frame = NULL,
nfolds = 0,
keep_cross_validation_models = TRUE,
keep_cross_validation_predictions = FALSE,
keep_cross_validation_fold_assignment = FALSE,
score_each_iteration = FALSE,
fold_assignment = c("AUTO", "Random", "Modulo", "Stratified"),
fold_column = NULL,
ignore_const_cols = TRUE,
offset_column = NULL,
weights_column = NULL,
stopping_rounds = 0,
stopping_metric = c("AUTO", "deviance", "logloss", "MSE", "RMSE", "MAE", "RMSLE",
"AUC", "AUCPR", "lift_top_group", "misclassification", "mean_per_class_error",
"custom", "custom_increasing"),
stopping_tolerance = 0.001,
max_runtime_secs = 0,
seed = -1,
distribution = c("AUTO", "bernoulli", "multinomial", "gaussian", "poisson", "gamma",
"tweedie", "laplace", "quantile", "huber"),
tweedie_power = 1.5,
categorical_encoding = c("AUTO", "Enum", "OneHotInternal", "OneHotExplicit", "Binary",
"Eigen", "LabelEncoder", "SortByResponse", "EnumLimited"),
quiet_mode = TRUE,
checkpoint = NULL,
export_checkpoints_dir = NULL,
ntrees = 50,
max_depth = 6,
min_rows = 1,
min_child_weight = 1,
learn_rate = 0.3,
eta = 0.3,
sample_rate = 1,
subsample = 1,
col_sample_rate = 1,
colsample_bylevel = 1,
col_sample_rate_per_tree = 1,
colsample_bytree = 1,
colsample_bynode = 1,
max_abs_leafnode_pred = 0,
max_delta_step = 0,
monotone_constraints = NULL,
interaction_constraints = NULL,
score_tree_interval = 0,
min_split_improvement = 0,
gamma = 0,
nthread = -1,
save_matrix_directory = NULL,
build_tree_one_node = FALSE,
parallelize_cross_validation = TRUE,
calibrate_model = FALSE,
calibration_frame = NULL,
calibration_method = c("AUTO", "PlattScaling", "IsotonicRegression"),
max_bins = 256,
max_leaves = 0,
sample_type = c("uniform", "weighted"),
normalize_type = c("tree", "forest"),
rate_drop = 0,
one_drop = FALSE,
skip_drop = 0,
tree_method = c("auto", "exact", "approx", "hist"),
grow_policy = c("depthwise", "lossguide"),
booster = c("gbtree", "gblinear", "dart"),
reg_lambda = 1,
reg_alpha = 0,
dmatrix_type = c("auto", "dense", "sparse"),
backend = c("auto", "gpu", "cpu"),
gpu_id = NULL,
gainslift_bins = -1,
auc_type = c("AUTO", "NONE", "MACRO_OVR", "WEIGHTED_OVR", "MACRO_OVO", "WEIGHTED_OVO"),
scale_pos_weight = 1,
eval_metric = NULL,
score_eval_metric_only = FALSE,
verbose = FALSE
)
```

- x
(Optional) A vector containing the names or indices of the predictor variables to use in building the model. If x is missing, then all columns except y are used.

- y
The name or column index of the response variable in the data. The response must be either a numeric or a categorical/factor variable. If the response is numeric, then a regression model will be trained, otherwise it will train a classification model.

- training_frame
Id of the training data frame.

- model_id
Destination id for this model; auto-generated if not specified.

- validation_frame
Id of the validation data frame.

- nfolds
Number of folds for K-fold cross-validation (0 to disable or >= 2). Defaults to 0.

- keep_cross_validation_models
`Logical`

. Whether to keep the cross-validation models. Defaults to TRUE.- keep_cross_validation_predictions
`Logical`

. Whether to keep the predictions of the cross-validation models. Defaults to FALSE.- keep_cross_validation_fold_assignment
`Logical`

. Whether to keep the cross-validation fold assignment. Defaults to FALSE.- score_each_iteration
`Logical`

. Whether to score during each iteration of model training. Defaults to FALSE.- fold_assignment
Cross-validation fold assignment scheme, if fold_column is not specified. The 'Stratified' option will stratify the folds based on the response variable, for classification problems. Must be one of: "AUTO", "Random", "Modulo", "Stratified". Defaults to AUTO.

- fold_column
Column with cross-validation fold index assignment per observation.

- ignore_const_cols
`Logical`

. Ignore constant columns. Defaults to TRUE.- offset_column
Offset column. This will be added to the combination of columns before applying the link function.

- weights_column
Column with observation weights. Giving some observation a weight of zero is equivalent to excluding it from the dataset; giving an observation a relative weight of 2 is equivalent to repeating that row twice. Negative weights are not allowed. Note: Weights are per-row observation weights and do not increase the size of the data frame. This is typically the number of times a row is repeated, but non-integer values are supported as well. During training, rows with higher weights matter more, due to the larger loss function pre-factor. If you set weight = 0 for a row, the returned prediction frame at that row is zero and this is incorrect. To get an accurate prediction, remove all rows with weight == 0.

- stopping_rounds
Early stopping based on convergence of stopping_metric. Stop if simple moving average of length k of the stopping_metric does not improve for k:=stopping_rounds scoring events (0 to disable) Defaults to 0.

- stopping_metric
Metric to use for early stopping (AUTO: logloss for classification, deviance for regression and anomaly_score for Isolation Forest). Note that custom and custom_increasing can only be used in GBM and DRF with the Python client. Must be one of: "AUTO", "deviance", "logloss", "MSE", "RMSE", "MAE", "RMSLE", "AUC", "AUCPR", "lift_top_group", "misclassification", "mean_per_class_error", "custom", "custom_increasing". Defaults to AUTO.

- stopping_tolerance
Relative tolerance for metric-based stopping criterion (stop if relative improvement is not at least this much) Defaults to 0.001.

- max_runtime_secs
Maximum allowed runtime in seconds for model training. Use 0 to disable. Defaults to 0.

- seed
Seed for random numbers (affects certain parts of the algo that are stochastic and those might or might not be enabled by default). Defaults to -1 (time-based random number).

- distribution
Distribution function Must be one of: "AUTO", "bernoulli", "multinomial", "gaussian", "poisson", "gamma", "tweedie", "laplace", "quantile", "huber". Defaults to AUTO.

- tweedie_power
Tweedie power for Tweedie regression, must be between 1 and 2. Defaults to 1.5.

- categorical_encoding
Encoding scheme for categorical features Must be one of: "AUTO", "Enum", "OneHotInternal", "OneHotExplicit", "Binary", "Eigen", "LabelEncoder", "SortByResponse", "EnumLimited". Defaults to AUTO.

- quiet_mode
`Logical`

. Enable quiet mode Defaults to TRUE.- checkpoint
Model checkpoint to resume training with.

- export_checkpoints_dir
Automatically export generated models to this directory.

- ntrees
(same as n_estimators) Number of trees. Defaults to 50.

- max_depth
Maximum tree depth (0 for unlimited). Defaults to 6.

- min_rows
(same as min_child_weight) Fewest allowed (weighted) observations in a leaf. Defaults to 1.

- min_child_weight
(same as min_rows) Fewest allowed (weighted) observations in a leaf. Defaults to 1.

- learn_rate
(same as eta) Learning rate (from 0.0 to 1.0) Defaults to 0.3.

- eta
(same as learn_rate) Learning rate (from 0.0 to 1.0) Defaults to 0.3.

- sample_rate
(same as subsample) Row sample rate per tree (from 0.0 to 1.0) Defaults to 1.

- subsample
(same as sample_rate) Row sample rate per tree (from 0.0 to 1.0) Defaults to 1.

- col_sample_rate
(same as colsample_bylevel) Column sample rate (from 0.0 to 1.0) Defaults to 1.

- colsample_bylevel
(same as col_sample_rate) Column sample rate (from 0.0 to 1.0) Defaults to 1.

- col_sample_rate_per_tree
(same as colsample_bytree) Column sample rate per tree (from 0.0 to 1.0) Defaults to 1.

- colsample_bytree
(same as col_sample_rate_per_tree) Column sample rate per tree (from 0.0 to 1.0) Defaults to 1.

- colsample_bynode
Column sample rate per tree node (from 0.0 to 1.0) Defaults to 1.

- max_abs_leafnode_pred
(same as max_delta_step) Maximum absolute value of a leaf node prediction Defaults to 0.0.

- max_delta_step
(same as max_abs_leafnode_pred) Maximum absolute value of a leaf node prediction Defaults to 0.0.

- monotone_constraints
A mapping representing monotonic constraints. Use +1 to enforce an increasing constraint and -1 to specify a decreasing constraint.

- interaction_constraints
A set of allowed column interactions.

- score_tree_interval
Score the model after every so many trees. Disabled if set to 0. Defaults to 0.

- min_split_improvement
(same as gamma) Minimum relative improvement in squared error reduction for a split to happen Defaults to 0.0.

- gamma
(same as min_split_improvement) Minimum relative improvement in squared error reduction for a split to happen Defaults to 0.0.

- nthread
Number of parallel threads that can be used to run XGBoost. Cannot exceed H2O cluster limits (-nthreads parameter). Defaults to maximum available Defaults to -1.

- save_matrix_directory
Directory where to save matrices passed to XGBoost library. Useful for debugging.

- build_tree_one_node
`Logical`

. Run on one node only; no network overhead but fewer cpus used. Suitable for small datasets. Defaults to FALSE.- parallelize_cross_validation
`Logical`

. Allow parallel training of cross-validation models Defaults to TRUE.- calibrate_model
`Logical`

. Use Platt Scaling (default) or Isotonic Regression to calculate calibrated class probabilities. Calibration can provide more accurate estimates of class probabilities. Defaults to FALSE.- calibration_frame
Data for model calibration

- calibration_method
Calibration method to use Must be one of: "AUTO", "PlattScaling", "IsotonicRegression". Defaults to AUTO.

- max_bins
For tree_method=hist only: maximum number of bins Defaults to 256.

- max_leaves
For tree_method=hist only: maximum number of leaves Defaults to 0.

- sample_type
For booster=dart only: sample_type Must be one of: "uniform", "weighted". Defaults to uniform.

- normalize_type
For booster=dart only: normalize_type Must be one of: "tree", "forest". Defaults to tree.

- rate_drop
For booster=dart only: rate_drop (0..1) Defaults to 0.0.

- one_drop
`Logical`

. For booster=dart only: one_drop Defaults to FALSE.- skip_drop
For booster=dart only: skip_drop (0..1) Defaults to 0.0.

- tree_method
Tree method Must be one of: "auto", "exact", "approx", "hist". Defaults to auto.

- grow_policy
Grow policy - depthwise is standard GBM, lossguide is LightGBM Must be one of: "depthwise", "lossguide". Defaults to depthwise.

- booster
Booster type Must be one of: "gbtree", "gblinear", "dart". Defaults to gbtree.

- reg_lambda
L2 regularization Defaults to 1.0.

- reg_alpha
L1 regularization Defaults to 0.0.

- dmatrix_type
Type of DMatrix. For sparse, NAs and 0 are treated equally. Must be one of: "auto", "dense", "sparse". Defaults to auto.

- backend
Backend. By default (auto), a GPU is used if available. Must be one of: "auto", "gpu", "cpu". Defaults to auto.

- gpu_id
Which GPU(s) to use.

- gainslift_bins
Gains/Lift table number of bins. 0 means disabled.. Default value -1 means automatic binning. Defaults to -1.

- auc_type
Set default multinomial AUC type. Must be one of: "AUTO", "NONE", "MACRO_OVR", "WEIGHTED_OVR", "MACRO_OVO", "WEIGHTED_OVO". Defaults to AUTO.

- scale_pos_weight
Controls the effect of observations with positive labels in relation to the observations with negative labels on gradient calculation. Useful for imbalanced problems. Defaults to 1.0.

- eval_metric
Specification of evaluation metric that will be passed to the native XGBoost backend.

- score_eval_metric_only
`Logical`

. If enabled, score only the evaluation metric. This can make model training faster if scoring is frequent (eg. each iteration). Defaults to FALSE.- verbose
`Logical`

. Print scoring history to the console (Metrics per tree). Defaults to FALSE.

```
if (FALSE) {
library(h2o)
h2o.init()
# Import the titanic dataset
f <- "https://s3.amazonaws.com/h2o-public-test-data/smalldata/gbm_test/titanic.csv"
titanic <- h2o.importFile(f)
# Set predictors and response; set response as a factor
titanic['survived'] <- as.factor(titanic['survived'])
predictors <- setdiff(colnames(titanic), colnames(titanic)[2:3])
response <- "survived"
# Split the dataset into train and valid
splits <- h2o.splitFrame(data = titanic, ratios = .8, seed = 1234)
train <- splits[[1]]
valid <- splits[[2]]
# Train the XGB model
titanic_xgb <- h2o.xgboost(x = predictors, y = response,
training_frame = train, validation_frame = valid,
booster = "dart", normalize_type = "tree",
seed = 1234)
}
```

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