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, uplift_metric = c("AUTO", "KL", "Euclidean", "ChiSquared"), auuc_type = c("AUTO", "qini", "lift", "gain"), auuc_nbins = -1, 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. |
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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 |
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score_tree_interval | Score the model after every so many trees. Disabled if set to 0. Defaults to 0. |
ignore_const_cols |
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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 sqrtp 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 |
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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 |
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Creates a H2OModel object of the right type.
predict.H2OModel
for prediction