Builds a Uplift Random Forest model on an H2OFrame.
h2o.upliftRandomForest( x, y, training_frame, treatment_column, model_id = NULL, validation_frame = NULL, score_each_iteration = FALSE, score_tree_interval = 0, ignore_const_cols = TRUE, ntrees = 50, max_depth = 20, min_rows = 1, nbins = 20, nbins_top_level = 1024, nbins_cats = 1024, max_runtime_secs = 0, seed = 1, mtries = 2, sample_rate = 0.632, sample_rate_per_class = NULL, col_sample_rate_change_per_level = 1, col_sample_rate_per_tree = 1, histogram_type = c("AUTO", "UniformAdaptive", "Random", "QuantilesGlobal", "RoundRobin", "UniformRobust"), categorical_encoding = c("AUTO", "Enum", "OneHotInternal", "OneHotExplicit", "Binary", "Eigen", "LabelEncoder", "SortByResponse", "EnumLimited"), distribution = c("AUTO", "bernoulli"), check_constant_response = TRUE, custom_metric_func = NULL, uplift_metric = c("AUTO", "KL", "Euclidean", "ChiSquared"), auuc_type = c("AUTO", "qini", "lift", "gain"), auuc_nbins = 1, stopping_rounds = 0, stopping_metric = c("AUTO", "AUUC", "ATE", "ATT", "ATC", "qini"), stopping_tolerance = 0.001, 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. 
treatment_column  Define the column which will be used for computing uplift gain to select best split for a tree. The column has to divide the dataset into treatment (value 1) and control (value 0) groups. Defaults to treatment. 
model_id  Destination id for this model; autogenerated if not specified. 
validation_frame  Id of the validation data frame. 
score_each_iteration 

score_tree_interval  Score the model after every so many trees. Disabled if set to 0. Defaults to 0. 
ignore_const_cols 

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. 
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). 
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 2. 
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 
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. 
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. 
distribution  Distribution function Must be one of: "AUTO", "bernoulli". Defaults to AUTO. 
check_constant_response 

custom_metric_func  Reference to custom evaluation function, format: `language:keyName=funcName` 
uplift_metric  Divergence metric used to find best split when building an uplift tree. Must be one of: "AUTO", "KL", "Euclidean", "ChiSquared". Defaults to AUTO. 
auuc_type  Metric used to calculate Area Under Uplift Curve. Must be one of: "AUTO", "qini", "lift", "gain". Defaults to AUTO. 
auuc_nbins  Number of bins to calculate Area Under Uplift Curve. Defaults to 1. 
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", "AUUC", "ATE", "ATT", "ATC", "qini". 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. 
verbose 

Creates a H2OModel object of the right type.
predict.H2OModel
for prediction