Usage
h2o.randomForest(x, y, training_frame, model_id, validation_frame, checkpoint,
mtries = -1, sample_rate = 0.632, build_tree_one_node = FALSE,
ntrees = 50, max_depth = 20, min_rows = 1, nbins = 20,
nbins_cats = 1024, binomial_double_trees = FALSE,
balance_classes = FALSE, max_after_balance_size = 5, seed,
offset_column = NULL, weights_column = NULL, nfolds = 0,
fold_column = NULL, fold_assignment = c("AUTO", "Random", "Modulo"),
keep_cross_validation_predictions = FALSE, ...)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 1, 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.
validation_frame
An H2OFrame object containing the variables in the model.
checkpoint
"Model checkpoint (either key or H2ODeepLearningModel) to resume training with."
mtries
Number of variables randomly sampled as candidates at each split.
If set to -1, defaults to sqrt{p} for classification, and p/3 for regression,
where p is the number of predictors.
sample_rate
Sample rate, from 0 to 1.0.
build_tree_one_node
Run on one node only; no network overhead but
fewer cpus used. Suitable for small datasets.
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.
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.
binomial_double_trees
For binary classification: Build 2x as many trees (one per class) - can lead to higher accuracy.
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) - Note: only
reproducible when running single threaded
offset_column
Specify the offset column.
weights_column
Specify the weights column.
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
...
(Currently Unimplemented)