Builds a Random Forest model on an H2OFrame.

```
h2o.randomForest(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 = 20, min_rows = 1,
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,
mtries = -1, sample_rate = 0.6320000291, sample_rate_per_class = NULL,
binomial_double_trees = FALSE, checkpoint = NULL,
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"),
categorical_encoding = c("AUTO", "Enum", "OneHotInternal", "OneHotExplicit",
"Binary", "Eigen", "LabelEncoder", "SortByResponse", "EnumLimited"),
calibrate_model = FALSE, calibration_frame = NULL,
distribution = c("AUTO", "bernoulli", "multinomial", "gaussian", "poisson",
"gamma", "tweedie", "laplace", "quantile", "huber"),
custom_metric_func = NULL, 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_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 argument is deprecated and has no use for Random Forest.

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 20.

min_rows

Fewest allowed (weighted) observations in a leaf. Defaults to 1.

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.

mtries

Number of variables randomly sampled as candidates at each split. If set to -1, defaults to sqrtp for classification and p/3 for regression (where p is the # of predictors Defaults to -1.

sample_rate

Row sample rate per tree (from 0.0 to 1.0) Defaults to 0.6320000291.

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

binomial_double_trees

`Logical`

. For binary classification: Build 2x as many trees (one per class) - can lead to higher
accuracy. Defaults to FALSE.

checkpoint

Model checkpoint to resume training with.

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.

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

distribution

Distribution. This argument is deprecated and has no use for Random Forest.

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.

`predict.H2OModel`

for prediction