h2o.predict_rules function

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.

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.

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

## Not run: 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")) ## End(Not run)
  • Maintainer: Tomas Fryda
  • License: Apache License (== 2.0)
  • Last published: 2024-01-11