Build gradient boosted classification or regression trees

Source: R/gbm.R

Builds gradient boosted classification trees and gradient boosted regression trees on a parsed data set. The default distribution function will guess the model type based on the response column type. In order to run properly, the response column must be an numeric for "gaussian" or an enum for "bernoulli" or "multinomial".

h2o.gbm(x, y, training_frame, model_id = NULL, validation_frame = NULL,
  nfolds = 0, keep_cross_validation_predictions = FALSE,
  keep_cross_validation_fold_assignment = FALSE,
  score_each_iteration = FALSE, score_tree_interval = 0,
  fold_assignment = c("AUTO", "Random", "Modulo", "Stratified"),
  fold_column = NULL, ignore_const_cols = TRUE, offset_column = NULL,
  weights_column = NULL, balance_classes = FALSE,
  class_sampling_factors = NULL, max_after_balance_size = 5,
  max_hit_ratio_k = 0, ntrees = 50, max_depth = 5, min_rows = 10,
  nbins = 20, nbins_top_level = 1024, nbins_cats = 1024,
  r2_stopping = Inf, 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, build_tree_one_node = FALSE,
  learn_rate = 0.1, learn_rate_annealing = 1, distribution = c("AUTO",
  "bernoulli", "quasibinomial", "multinomial", "gaussian", "poisson", "gamma",
  "tweedie", "laplace", "quantile", "huber"), quantile_alpha = 0.5,
  tweedie_power = 1.5, huber_alpha = 0.9, checkpoint = NULL,
  sample_rate = 1, sample_rate_per_class = NULL, col_sample_rate = 1,
  col_sample_rate_change_per_level = 1, col_sample_rate_per_tree = 1,
  min_split_improvement = 1e-05, histogram_type = c("AUTO",
  "UniformAdaptive", "Random", "QuantilesGlobal", "RoundRobin"),
  max_abs_leafnode_pred = Inf, pred_noise_bandwidth = 0,
  categorical_encoding = c("AUTO", "Enum", "OneHotInternal", "OneHotExplicit",
  "Binary", "Eigen", "LabelEncoder", "SortByResponse", "EnumLimited"),
  calibrate_model = FALSE, calibration_frame = NULL,
  custom_metric_func = NULL, 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_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.
score_tree_interval	Score the model after every so many trees. Disabled if set to 0. Defaults to 0.
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.
balance_classes	Logical. Balance training data class counts via over/under-sampling (for imbalanced data). Defaults to FALSE.
class_sampling_factors	Desired over/under-sampling ratios per class (in lexicographic order). If not specified, sampling factors will be automatically computed to obtain class balance during training. Requires balance_classes.
max_after_balance_size	Maximum relative size of the training data after balancing class counts (can be less than 1.0). Requires balance_classes. Defaults to 5.0.
max_hit_ratio_k	Max. number (top K) of predictions to use for hit ratio computation (for multi-class only, 0 to disable) Defaults to 0.
ntrees	Number of trees. Defaults to 50.
max_depth	Maximum tree depth. Defaults to 5.
min_rows	Fewest allowed (weighted) observations in a leaf. Defaults to 10.
nbins	For numerical columns (real/int), build a histogram of (at least) this many bins, then split at the best point Defaults to 20.
nbins_top_level	For numerical columns (real/int), build a histogram of (at most) this many bins at the root level, then decrease by factor of two per level Defaults to 1024.
nbins_cats	For categorical columns (factors), build a histogram of this many bins, then split at the best point. Higher values can lead to more overfitting. Defaults to 1024.
r2_stopping	r2_stopping is no longer supported and will be ignored if set - please use stopping_rounds, stopping_metric and stopping_tolerance instead. Previous version of H2O would stop making trees when the R^2 metric equals or exceeds this Defaults to 1.797693135e+308.
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).
build_tree_one_node	Logical. Run on one node only; no network overhead but fewer cpus used. Suitable for small datasets. Defaults to FALSE.
learn_rate	Learning rate (from 0.0 to 1.0) Defaults to 0.1.
learn_rate_annealing	Scale the learning rate by this factor after each tree (e.g., 0.99 or 0.999) Defaults to 1.
distribution	Distribution function Must be one of: "AUTO", "bernoulli", "quasibinomial", "multinomial", "gaussian", "poisson", "gamma", "tweedie", "laplace", "quantile", "huber". Defaults to AUTO.
quantile_alpha	Desired quantile for Quantile regression, must be between 0 and 1. Defaults to 0.5.
tweedie_power	Tweedie power for Tweedie regression, must be between 1 and 2. Defaults to 1.5.
huber_alpha	Desired quantile for Huber/M-regression (threshold between quadratic and linear loss, must be between 0 and 1). Defaults to 0.9.
checkpoint	Model checkpoint to resume training with.
sample_rate	Row sample rate per tree (from 0.0 to 1.0) Defaults to 1.
sample_rate_per_class	A list of row sample rates per class (relative fraction for each class, from 0.0 to 1.0), for each tree
col_sample_rate	Column sample rate (from 0.0 to 1.0) Defaults to 1.
col_sample_rate_change_per_level	Relative change of the column sampling rate for every level (must be > 0.0 and <= 2.0) Defaults to 1.
col_sample_rate_per_tree	Column sample rate per tree (from 0.0 to 1.0) Defaults to 1.
min_split_improvement	Minimum relative improvement in squared error reduction for a split to happen Defaults to 1e-05.
histogram_type	What type of histogram to use for finding optimal split points Must be one of: "AUTO", "UniformAdaptive", "Random", "QuantilesGlobal", "RoundRobin". Defaults to AUTO.
max_abs_leafnode_pred	Maximum absolute value of a leaf node prediction Defaults to 1.797693135e+308.
pred_noise_bandwidth	Bandwidth (sigma) of Gaussian multiplicative noise ~N(1,sigma) for tree node predictions Defaults to 0.
categorical_encoding	Encoding scheme for categorical features Must be one of: "AUTO", "Enum", "OneHotInternal", "OneHotExplicit", "Binary", "Eigen", "LabelEncoder", "SortByResponse", "EnumLimited". Defaults to AUTO.
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
custom_metric_func	Reference to custom evaluation function, format: `language:keyName=funcName`
verbose	Logical. Print scoring history to the console (Metrics per tree for GBM, DRF, & XGBoost. Metrics per epoch for Deep Learning). Defaults to FALSE.

See also

predict.H2OModel for prediction

Examples

# NOT RUN {
library(h2o)
h2o.init()

# Run regression GBM on australia.hex data
ausPath <- system.file("extdata", "australia.csv", package="h2o")
australia.hex <- h2o.uploadFile(path = ausPath)
independent <- c("premax", "salmax","minairtemp", "maxairtemp", "maxsst",
"maxsoilmoist", "Max_czcs")
dependent <- "runoffnew"
h2o.gbm(y = dependent, x = independent, training_frame = australia.hex,
ntrees = 3, max_depth = 3, min_rows = 2)
# }