tl_add_cluster_features: Cluster-Based Features

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

Cluster-Based Features

Description

Add cluster assignments as features for supervised learning. This semi-supervised approach can capture non-linear patterns.

Usage

tl_add_cluster_features(data, response = NULL, method = "kmeans", ...)

Arguments

data	A data frame
response	Response variable name (will be excluded from clustering)
method	Clustering method: "kmeans", "pam", "hclust", "dbscan"
...	Additional arguments for clustering

Value

Original data with cluster assignment column(s) added

Examples

# Add cluster features before supervised learning
data_with_clusters <- tl_add_cluster_features(iris, response = "Species",
                                                method = "kmeans", k = 3)
model <- tl_model(data_with_clusters, Species ~ ., method = "forest")

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