build_feature_engineering_agent: Build a Feature Engineering Agent

In LLMAgentR: Language Model Agents in R for AI Workflows and Research

Build a Feature Engineering Agent

Description

Constructs a graph-based feature engineering agent that guides the process of: recommending, generating, executing, fixing, and explaining feature engineering code.

Arguments

model	A function that accepts a prompt and returns an LLM-generated response.
human_validation	Logical; include a manual review node before code execution.
bypass_recommended_steps	Logical; skip the LLM-based recommendation phase.
bypass_explain_code	Logical; skip final explanation step.
verbose	Logical; whether to print progress messages (default: TRUE)

Value

A callable agent function that executes feature engineering via a state graph.

Examples

## Not run: 
# 1) Load the data
data <- read.csv("tests/testthat/test-data/churn_data.csv")

# 2) Create the feature engineering agent
feature_engineering_agent <- build_feature_engineering_agent(
  model = my_llm_wrapper,
  human_validation = FALSE,
  bypass_recommended_steps = FALSE,
  bypass_explain_code = FALSE,
  verbose = TRUE
)

# 3) Define the initial state
initial_state <- list(
  data_raw = data,
  target_variable = "Churn",
  user_instructions = "Inspect the data. Make any new features and transformations
  that you think will be useful for predicting the target variable.",
  max_retries = 3,
  retry_count = 0
)

# 4) Run the agent
final_state <- feature_engineering_agent(initial_state)

## End(Not run)

LLMAgentR documentation built on June 8, 2025, 10:02 a.m.