Builds a Distributed RuleFit model on a parsed dataset, for regression or classification.
h2o.rulefit( x, y, training_frame, model_id = NULL, validation_frame = NULL, seed = -1, algorithm = c("AUTO", "DRF", "GBM"), min_rule_length = 3, max_rule_length = 3, max_num_rules = -1, model_type = c("rules_and_linear", "rules", "linear"), weights_column = NULL, distribution = c("AUTO", "bernoulli", "multinomial", "gaussian", "poisson", "gamma", "tweedie", "laplace", "quantile", "huber"), rule_generation_ntrees = 50, auc_type = c("AUTO", "NONE", "MACRO_OVR", "WEIGHTED_OVR", "MACRO_OVO", "WEIGHTED_OVO"), remove_duplicates = TRUE )
| 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. |
| 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 |
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# 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 test splits <- 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) # }