tl_anomaly_aware: Anomaly-Aware Supervised Learning

In tidylearn: A Unified Tidy Interface to R's Machine Learning Ecosystem

Anomaly-Aware Supervised Learning

Description

Detect outliers using DBSCAN or other methods, then optionally remove them or down-weight them before supervised learning.

Usage

tl_anomaly_aware(
  data,
  formula,
  response,
  anomaly_method = "dbscan",
  action = "flag",
  supervised_method = "logistic",
  ...
)

Arguments

data	A data frame
formula	Model formula
response	Response variable name
anomaly_method	Method for anomaly detection: "dbscan", "isolation_forest"
action	Action to take: "remove", "flag", "downweight"
supervised_method	Supervised learning method
...	Additional arguments

Value

A tidylearn model or list with model and anomaly info

Examples

model <- tl_anomaly_aware(iris, Species ~ ., response = "Species",
                           anomaly_method = "dbscan", action = "flag")

tidylearn documentation built on Feb. 6, 2026, 5:07 p.m.