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Random forest classifier for factor embeddings

Source: R/classifier_metrics.R
classify_rf.Rd

Trains a random forest on factor embeddings using the randomForest package (must be installed) and evaluates on test samples.

Usage

classify_rf(
  embedding,
  labels,
  test_fraction = 0.2,
  test_idx = NULL,
  ntree = 500L,
  seed = NULL
)

Arguments

embedding

Numeric matrix where rows are samples and columns are features (e.g., t(result$H) for sample embeddings).

labels

Integer or factor vector of class labels. Length must equal nrow(embedding).

test_fraction

Fraction of samples held out for testing (default 0.2).

test_idx

Optional integer vector of test indices. If provided, test_fraction is ignored.

ntree

Number of trees (default 500).

seed

Random seed for train/test split reproducibility.

Value

An fn_classifier_eval object (same structure as KNN variant).

See also

classify_embedding, classify_logistic

Examples

# \donttest{
# Random forest on random embeddings (requires randomForest package)
if (requireNamespace("randomForest", quietly = TRUE)) {
  set.seed(42)
  embed <- matrix(rnorm(200), nrow = 40, ncol = 5)
  labels <- factor(rep(1:4, each = 10))
  eval <- classify_rf(embed, labels, ntree = 100, seed = 1)
  eval$accuracy
}
#> [1] 0.25
# }