h2o (version 3.38.0.1)

h2o.predict_rules: Evaluates validity of the given rules on the given data. Returns a frame with a column per each input rule id, representing a flag whether given rule is applied to the observation or not.

Description

Evaluates validity of the given rules on the given data. Returns a frame with a column per each input rule id, representing a flag whether given rule is applied to the observation or not.

Usage

h2o.predict_rules(model, frame, rule_ids)

Arguments

model

A trained rulefit model.

frame

A frame on which rule validity is to be evaluated

rule_ids

Rule ids to be evaluated against the frame

Examples

if (FALSE) {
library(h2o)
h2o.init()
titanic <- h2o.importFile(
 "https://s3.amazonaws.com/h2o-public-test-data/smalldata/gbm_test/titanic.csv"
)
response = "survived"
predictors <- c("age", "sibsp", "parch", "fare", "sex", "pclass")
titanic[,response] <- as.factor(titanic[,response])
titanic[,"pclass"] <- as.factor(titanic[,"pclass"])

splits <- h2o.splitFrame(data = titanic, ratios = .8, seed = 1234)
train <- splits[[1]]
test <- splits[[2]]

rfit <- h2o.rulefit(y = response, x = predictors, training_frame = train, validation_frame = test, 
min_rule_length = 1, max_rule_length = 10, max_num_rules = 100, seed = 1, model_type="rules")
h2o.predict_rules(rfit, train, c("M1T0N7, M1T49N7, M1T16N7", "M1T36N7", "M2T19N19"))
}