Cluster-Based Features — tl_add_cluster

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

The original data frame with an additional factor column named cluster_<method> containing cluster assignments. The fitted cluster model is stored as an attribute "cluster_model".

Examples

# \donttest{
# 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")
# }