Semi-Supervised Learning via Clustering

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 = "tree",
  ...
)

Arguments

data

A data frame

formula

Model formula. The response must be a factor, character or logical column.

labeled_indices

Indices of labeled observations

cluster_method

Clustering method for label propagation

supervised_method

Supervised learning method for the final model (default: "tree", which handles any number of classes). "logistic" is binary-only and errors on a response with more than two levels.

...

Additional arguments

Value

A tidylearn model object with additional class "tidylearn_semisupervised", trained on pseudo-labeled data. The model includes a semisupervised_info element with labeled_indices, cluster_model, label_mapping, and n_unlabelled_dropped, the number of rows left out because their cluster had no labelled observation.

Details

Labels are propagated by majority vote within each cluster, so the response must be categorical. Rows in a cluster that holds no labelled observation have no label to take; they are left out of training, with a warning giving the count.

Examples

# \donttest{
# 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 = "tree"
)
# }