Extract rules from a Cubist model

Extracts rule conditions from a Cubist regression model as R expressions. Each rule consists of conditions that define when a linear model applies.

Usage

# S3 method for class 'cubist'
extract_rules(x, committee = 1L, ...)

Arguments

x

A cubist object from the Cubist package.

committee

An integer vector specifying which committee(s) to extract rules from. Defaults to 1L (first committee). Values must be between 1 and the total number of committees in the model.

...

Not used.

Value

A tibble with columns:

• committee: Integer committee number

• id: Integer rule number within the committee

• rules: List column containing R expressions for each rule's conditions

Details

Cubist models use committees (similar to boosting iterations) where each committee contains multiple rules. Each rule has:

• Conditions that determine when the rule applies (splits on predictors)

• A linear model that makes predictions when conditions are met

This function extracts the conditions as R expressions that can be evaluated on data. Rules with no conditions (applying to all data) return TRUE.

The expressions use standard R operators:

• Continuous splits: >, <=, etc.

• Categorical single value: ==

• Categorical multiple values: %in%

• Missing values: is.na()

See also

rules::tidy.cubist() for extracting rules as text strings

Examples

if (FALSE) { # \dontrun{
library(Cubist)
library(lorax)

# Create sample data
set.seed(1)
n <- 100
p <- 5
X <- matrix(rnorm(n * p), n, p)
colnames(X) <- paste0("x", 1:p)
y <- X[, 1] + X[, 2]^2 + rnorm(n)

# Fit Cubist model with multiple committees
mod <- cubist(X, y, committees = 3)

# Extract rules from first committee
rules <- extract_rules(mod)
rules

# Extract from multiple committees
rules_all <- extract_rules(mod, committee = 1:3)

# Convert to readable text
rule_text(rules$rules[[1]])
} # }