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", "multinomial", "gaussian", "poisson", "gamma",
"tweedie", "laplace", "quantile", "huber"),
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,
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.

- 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; auto-generated if not specified.

- validation_frame
Id of the validation data frame.

- 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.

- ignore_const_cols
`Logical`

. Ignore constant columns. Defaults to TRUE.- 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 (time-based 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", "multinomial", "gaussian", "poisson", "gamma", "tweedie", "laplace", "quantile", "huber". Defaults to AUTO.

- 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.- 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.

- verbose
`Logical`

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

`predict.H2OModel`

for prediction