Build an eXtreme Gradient Boosting model

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", "lift_top_group", "misclassification",
  "mean_per_class_error"), 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, 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, max_abs_leafnode_pred = 0,
  max_delta_step = 0, monotone_constraints = NULL,
  score_tree_interval = 0, min_split_improvement = 0, gamma = 0,
  nthread = -1, max_bins = 256, max_leaves = 0,
  min_sum_hessian_in_leaf = 100, min_data_in_leaf = 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 = 0, reg_alpha = 0,
  dmatrix_type = c("auto", "dense", "sparse"), backend = c("auto",
  "gpu", "cpu"), gpu_id = 0, verbose = FALSE)

Arguments

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.
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) Must be one of: "AUTO", "deviance", "logloss", "MSE", "RMSE", "MAE", "RMSLE", "AUC", "lift_top_group", "misclassification", "mean_per_class_error". 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.
ntrees	(same as n_estimators) Number of trees. Defaults to 50.
max_depth	Maximum tree depth. 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.
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.
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.
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.
min_sum_hessian_in_leaf	For tree_method=hist only: the mininum sum of hessian in a leaf to keep splitting Defaults to 100.0.
min_data_in_leaf	For tree_method=hist only: the mininum data in a leaf to keep splitting Defaults to 0.0.
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 0.0.
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 to use. Defaults to 0.
verbose	Logical. Print scoring history to the console (Metrics per tree for GBM, DRF, & XGBoost. Metrics per epoch for Deep Learning). Defaults to FALSE.