Measures average silhouette across all observations

Usage

silhouette_avg(object, ...)

# S3 method for class 'cluster_spec'
silhouette_avg(object, ...)

# S3 method for class 'cluster_fit'
silhouette_avg(object, new_data = NULL, dists = NULL, dist_fun = NULL, ...)

# S3 method for class 'workflow'
silhouette_avg(object, new_data = NULL, dists = NULL, dist_fun = NULL, ...)

silhouette_avg_vec(
  object,
  new_data = NULL,
  dists = NULL,
  dist_fun = philentropy::distance,
  ...
)

Arguments

object: A fitted kmeans tidyclust model
...: Other arguments passed to methods.
new_data: A dataset to predict on. If NULL, uses trained clustering.
dists: A distance matrix. Used if new_data is NULL.
dist_fun: A function of the form function(x) that takes a data frame or matrix and returns a dist object. Defaults to philentropy::distance with Euclidean distance. See philentropy::getDistMethods() for a list of supported methods, and vignette("tuning_and_metrics", package = "tidyclust") for usage examples.

Value

A double; the average silhouette.

Details

Not to be confused with silhouette() that returns a tibble with silhouette for each observation. The silhouette coefficient ranges from -1 to 1, where values close to 1 indicate well-separated clusters. This metric has direction = "maximize", so tune::select_best() and tune::show_best() will return models with the highest silhouette values.

Examples

kmeans_spec <- k_means(num_clusters = 5) |>
  set_engine("stats")

kmeans_fit <- fit(kmeans_spec, ~., mtcars)

dists <- mtcars |>
  as.matrix() |>
  dist()

silhouette_avg(kmeans_fit, dists = dists)
#> # A tibble: 1 × 3
#>   .metric        .estimator .estimate
#>   <chr>          <chr>          <dbl>
#> 1 silhouette_avg standard       0.345

silhouette_avg_vec(kmeans_fit, dists = dists)
#> [1] 0.3450963