LGDtoolkit (version 0.2.0)

rf.interaction.transformer: Extract interactions from random forest

Description

rf.interaction.transformer extracts the interactions from random forest. It implements customized random forest algorithm that takes into account different conditions (for single decision tree) such as minimum percentage of observations and defaults in each node, maximum tree depth and monotonicity condition at each splitting node. Sum of squared errors index is used as metric for node splitting .

Usage

rf.interaction.transformer(
  db,
  rf,
  target,
  num.rf = NA,
  num.tree,
  min.pct.obs,
  min.avg.rate,
  max.depth,
  monotonicity,
  create.interaction.rf,
  seed = 991
)

Value

The command rf.interaction.transformer returns a list of two data frames. The first data frame provides the trees summary. The second data frame is a new risk factor extracted from random forest.

Arguments

db

Data frame of risk factors and target variable supplied for interaction extraction.

rf

Character vector of risk factor names on which decision tree is run.

target

Name of target variable within db argument.

num.rf

Number of risk factors randomly selected for each decision tree. If default value (NA) is supplied, then number of risk factors will be calculated as sqrt(number of all supplied risk factors).

num.tree

Number of decision trees used for random forest.

min.pct.obs

Minimum percentage of observation in each leaf.

min.avg.rate

Minimum average target rate in each leaf.

max.depth

Maximum number of splits.

monotonicity

Logical indicator. If TRUE, observed trend between risk factor and target will be preserved in splitting node.

create.interaction.rf

Logical indicator. If TRUE, second element of the output will be data frame with interaction modalities.

seed

Random seed to ensure result reproducibility. Default is 991.

Examples

library(LGDtoolkit)
data(lgd.ds.c)
rf.it <- LGDtoolkit::rf.interaction.transformer(db = lgd.ds.c, 
		     rf = names(lgd.ds.c)[!names(lgd.ds.c)%in%"lgd"], 
		     target = "lgd",
		     num.rf = NA, 
		     num.tree = 3,
		     min.pct.obs = 0.05,
		     min.avg.rate = 0.01,
		     max.depth = 2,
		     monotonicity = TRUE,
		     create.interaction.rf = TRUE,
		     seed = 789)
names(rf.it)
rf.it[["tree.info"]]
tail(rf.it[["interaction"]])
table(rf.it[["interaction"]][, 1], useNA = "always")