h2o.xgboost | R Documentation |
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 |
|
keep_cross_validation_predictions |
|
keep_cross_validation_fold_assignment |
|
score_each_iteration |
|
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 |
|
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 |
|
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 |
|
parallelize_cross_validation |
|
calibrate_model |
|
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 |
|
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 |
|
verbose |
|
## 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)
## End(Not run)
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