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_models = TRUE,
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,
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", "AUCPR", "lift_top_group", "misclassification", "mean_per_class_error",
"custom", "custom_increasing"),
stopping_tolerance = 0.001,
max_runtime_secs = 0,
seed = -1,
build_tree_one_node = FALSE,
mtries = -1,
sample_rate = 0.632,
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", "UniformRobust"),
categorical_encoding = c("AUTO", "Enum", "OneHotInternal", "OneHotExplicit", "Binary",
"Eigen", "LabelEncoder", "SortByResponse", "EnumLimited"),
calibrate_model = FALSE,
calibration_frame = NULL,
calibration_method = c("AUTO", "PlattScaling", "IsotonicRegression"),
distribution = c("AUTO", "bernoulli", "multinomial", "gaussian", "poisson", "gamma",
"tweedie", "laplace", "quantile", "huber"),
custom_metric_func = NULL,
export_checkpoints_dir = NULL,
check_constant_response = TRUE,
gainslift_bins = -1,
auc_type = c("AUTO", "NONE", "MACRO_OVR", "WEIGHTED_OVR", "MACRO_OVO", "WEIGHTED_OVO"),
verbose = FALSE
)
```

Creates a H2OModel object of the right type.

- 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
`Logical`

. Whether to keep the cross-validation models. Defaults to TRUE.- 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. 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.

- 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.

- ntrees
Number of trees. Defaults to 50.

- max_depth
Maximum tree depth (0 for unlimited). 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 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).

- 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 sqrt{p} 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 0.632.

- 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", "UniformRobust". 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 (default) or Isotonic Regression to calculate calibrated class probabilities. Calibration can provide more accurate estimates of class probabilities. Defaults to FALSE.- calibration_frame
Data for model calibration

- calibration_method
Calibration method to use Must be one of: "AUTO", "PlattScaling", "IsotonicRegression". Defaults to AUTO.

- 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`

- export_checkpoints_dir
Automatically export generated models to this directory.

- check_constant_response
`Logical`

. Check if response column is constant. If enabled, then an exception is thrown if the response column is a constant value.If disabled, then model will train regardless of the response column being a constant value or not. Defaults to TRUE.- 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.

- verbose
`Logical`

. Print scoring history to the console (Metrics per tree). Defaults to FALSE.

`predict.H2OModel`

for prediction

```
if (FALSE) {
library(h2o)
h2o.init()
# Import the cars dataset
f <- "https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv"
cars <- h2o.importFile(f)
# Set predictors and response; set response as a factor
cars["economy_20mpg"] <- as.factor(cars["economy_20mpg"])
predictors <- c("displacement", "power", "weight", "acceleration", "year")
response <- "economy_20mpg"
# Train the DRF model
cars_drf <- h2o.randomForest(x = predictors, y = response,
training_frame = cars, nfolds = 5,
seed = 1234)
}
```

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