h2o (version 3.2.0.3)

h2o.gbm: Gradient Boosted Machines

Description

Builds gradient boosted classification trees, and gradient boosted regression trees on a parsed data set.

Usage

h2o.gbm(x, y, training_frame, model_id, checkpoint, distribution = c("AUTO",
  "gaussian", "bernoulli", "multinomial", "poisson", "gamma", "tweedie"),
  tweedie_power = 1.5, ntrees = 50, max_depth = 5, min_rows = 10,
  learn_rate = 0.1, nbins = 20, nbins_cats = 1024,
  validation_frame = NULL, balance_classes = FALSE,
  max_after_balance_size = 1, seed, build_tree_one_node = FALSE,
  nfolds = 0, fold_column = NULL, fold_assignment = c("AUTO", "Random",
  "Modulo"), keep_cross_validation_predictions = FALSE,
  score_each_iteration = FALSE, offset_column = NULL,
  weights_column = NULL, ...)

Arguments

x

A vector containing the names or indices of the predictor variables to use in building the GBM model.

y

The name or index of the response variable. If the data does not contain a header, this is the column index number starting at 0, and increasing from left to right. (The response must be either an integer or a categorical variable).

training_frame

An H2OFrame object containing the variables in the model.

model_id

(Optional) The unique id assigned to the resulting model. If none is given, an id will automatically be generated.

checkpoint

"Model checkpoint (either key or H2ODeepLearningModel) to resume training with."

distribution

A character string. The distribution function of the response. Must be "AUTO", "bernoulli", "multinomial", "poisson", "gamma", "tweedie" or "gaussian"

tweedie_power

Tweedie power (only for Tweedie distribution, must be between 1 and 2)

ntrees

A nonnegative integer that determines the number of trees to grow.

max_depth

Maximum depth to grow the tree.

min_rows

Minimum number of rows to assign to teminal nodes.

learn_rate

An integer from 0.0 to 1.0

nbins

For numerical columns (real/int), build a histogram of this many bins, then split at the best point

nbins_cats

For categorical columns (enum), build a histogram of this many bins, then split at the best point. Higher values can lead to more overfitting.

validation_frame

An H2OFrame object indicating the validation dataset used to contruct the confusion matrix. If left blank, this defaults to the training data when nfolds = 0

balance_classes

logical, indicates whether or not to balance training data class counts via over/under-sampling (for imbalanced data)

max_after_balance_size

Maximum relative size of the training data after balancing class counts (can be less than 1.0)

seed

Seed for random numbers (affects sampling when balance_classes=T)

build_tree_one_node

Run on one node only; no network overhead but fewer cpus used. Suitable for small datasets.

nfolds

(Optional) Number of folds for cross-validation. If nfolds >= 2, then validation must remain empty.

fold_column

(Optional) Column with cross-validation fold index assignment per observation

fold_assignment

Cross-validation fold assignment scheme, if fold_column is not specified Must be "AUTO", "Random" or "Modulo"

keep_cross_validation_predictions

Whether to keep the predictions of the cross-validation models

score_each_iteration

Attempts to score each tree.

offset_column

Specify the offset column.

weights_column

Specify the weights column.

...

extra arguments to pass on (currently no implemented)

Details

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".

See Also

predict.H2OModel for prediction.

Examples

library(h2o)
localH2O = h2o.init()

# Run regression GBM on australia.hex data
ausPath <- system.file("extdata", "australia.csv", package="h2o")
australia.hex <- h2o.uploadFile(localH2O, 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)