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; auto-generated if not specified.
validation_frame: Id of the validation data frame.
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).
algorithm: The algorithm to use to generate rules. Must be one of: "AUTO", "DRF", "GBM". Defaults to AUTO.
min_rule_length: Minimum length of rules. Defaults to 3.
max_rule_length: Maximum length of rules. Defaults to 3.
max_num_rules: The maximum number of rules to return. defaults to -1 which means the number of rules is selected by diminishing returns in model deviance. Defaults to -1.
model_type: Specifies type of base learners in the ensemble. Must be one of: "rules_and_linear", "rules", "linear". Defaults to rules_and_linear.
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 per-row observation weights and do not increase the size of the data frame. This is typically the number of times a row is repeated, but non-integer values are supported as well. During training, rows with higher weights matter more, due to the larger loss function pre-factor. If you set weight = 0 for a row, the returned prediction frame at that row is zero and this is incorrect. To get an accurate prediction, remove all rows with weight == 0.
distribution: Distribution function Must be one of: "AUTO", "bernoulli", "multinomial", "gaussian", "poisson", "gamma", "tweedie", "laplace", "quantile", "huber". Defaults to AUTO.
rule_generation_ntrees: Specifies the number of trees to build in the tree model. Defaults to 50. Defaults to 50.
auc_type: Set default multinomial AUC type. Must be one of: "AUTO", "NONE", "MACRO_OVR", "WEIGHTED_OVR", "MACRO_OVO", "WEIGHTED_OVO". Defaults to AUTO.
remove_duplicates: Logical. Whether to remove rules which are identical to an earlier rule. Defaults to true. Defaults to TRUE.
lambda: Lambda for LASSO regressor.
max_categorical_levels: For every categorical feature, only use this many most frequent categorical levels for model training. Only used for categorical_encoding == EnumLimited. Defaults to 10.
Examples
## Not run:library(h2o)h2o.init()# Import the titanic dataset:f <-"https://s3.amazonaws.com/h2o-public-test-data/smalldata/gbm_test/titanic.csv"coltypes <- list(by.col.name = c("pclass","survived"), types=c("Enum","Enum"))df <- h2o.importFile(f, col.types = coltypes)# Split the dataset into train and testsplits <- h2o.splitFrame(data = df, ratios =0.8, seed =1)train <- splits[[1]]test <- splits[[2]]# Set the predictors and response; set the factors:response <-"survived"predictors <- c("age","sibsp","parch","fare","sex","pclass")# Build and train the model:rfit <- h2o.rulefit(y = response, x = predictors, training_frame = train, max_rule_length =10, max_num_rules =100, seed =1)# Retrieve the rule importance:print(rfit@model$rule_importance)# Predict on the test data:h2o.predict(rfit, newdata = test)## End(Not run)