Build a Uplift Random Forest model

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
)

Arguments

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". 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.
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 metric-based stopping criterion (stop if relative improvement is not at least this much) Defaults to 0.001.
verbose	`Logical`. Print scoring history to the console (Metrics per tree). Defaults to FALSE.

Value

Creates a H2OModel object of the right type.

Arguments

Value

See also