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", "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,
  max_abs_leafnode_pred = 0,
  max_delta_step = 0,
  monotone_constraints = NULL,
  score_tree_interval = 0,
  min_split_improvement = 0,
  gamma = 0,
  nthread = -1,
  save_matrix_directory = NULL,
  build_tree_one_node = FALSE,
  calibrate_model = FALSE,
  calibration_frame = NULL,
  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 = 1,
  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 and anonomaly_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. 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.
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.
calibrate_model	Logical. Use Platt Scaling to calculate calibrated class probabilities. Calibration can provide more accurate estimates of class probabilities. Defaults to FALSE.
calibration_frame	Calibration frame for Platt Scaling
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 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 to use. Defaults to 0.
verbose	Logical. Print scoring history to the console (Metrics per tree). Defaults to FALSE.

Examples

# NOT RUN {
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)
# }