h2o (version 3.32.0.1)

h2o.rulefit: Build a RuleFit Model

Description

Builds a Distributed RuleFit model on a parsed dataset, for regression or classification.

Usage

h2o.rulefit(
  x,
  y,
  training_frame,
  model_id = 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
)

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.

model_id

Destination id for this model; auto-generated if not specified.

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.

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.

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 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)
# }

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