tl_semisupervised: Semi-Supervised Learning via Clustering

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

Semi-Supervised Learning via Clustering

Description

Train a supervised model with limited labels by first clustering the data and propagating labels within clusters.

Usage

tl_semisupervised(
  data,
  formula,
  labeled_indices,
  cluster_method = "kmeans",
  supervised_method = "logistic",
  ...
)

Arguments

data	A data frame
formula	Model formula
labeled_indices	Indices of labeled observations
cluster_method	Clustering method for label propagation
supervised_method	Supervised learning method for final model
...	Additional arguments

Value

A tidylearn model trained on pseudo-labeled data

Examples

# Use only 10% of labels
labeled_idx <- sample(nrow(iris), size = 15)
model <- tl_semisupervised(iris, Species ~ ., labeled_indices = labeled_idx,
                           cluster_method = "kmeans", supervised_method = "logistic")

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