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, 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", "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 = 1e05, 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, export_checkpoints_dir = NULL, check_constant_response = TRUE, 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; autogenerated if not specified. 
validation_frame  Id of the validation data frame. 
nfolds  Number of folds for Kfold crossvalidation (0 to disable or >= 2). Defaults to 0. 
keep_cross_validation_models 

keep_cross_validation_predictions 

keep_cross_validation_fold_assignment 

score_each_iteration 

score_tree_interval  Score the model after every so many trees. Disabled if set to 0. Defaults to 0. 
fold_assignment  Crossvalidation 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 crossvalidation fold index assignment per observation. 
ignore_const_cols 

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 perrow observation weights and do not increase the size of the data frame. This is typically the number of times a row is repeated, but noninteger values are supported as well. During training, rows with higher weights matter more, due to the larger loss function prefactor. 
balance_classes 

class_sampling_factors  Desired over/undersampling 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 multiclass 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 and anonomaly_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 metricbased 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 (timebased random number). 
build_tree_one_node 

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 0.632. 
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 

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 1e05. 
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 

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` 
export_checkpoints_dir  Automatically export generated models to this directory. 
check_constant_response 

verbose 

Creates a H2OModel object of the right type.
predict.H2OModel
for prediction
# NOT RUN { library(h2o) h2o.init() # Import the cars dataset f < "https://s3.amazonaws.com/h2opublictestdata/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) # }