Calculates Sum of Squared Error in each cluster

Calculates Sum of Squared Error in each cluster

Usage

sse_within(
  object,
  new_data = NULL,
  dist_fun = function(x, y) {
     philentropy::dist_many_many(x, y, method =
    "euclidean")
 }
)

Arguments

object

A fitted kmeans tidyclust model

new_data

A dataset to predict on. If NULL, uses trained clustering.

dist_fun

A function of the form function(x, y) that takes two matrices (centroids and observations) and returns a distance matrix. Defaults to philentropy::dist_many_many with Euclidean distance. See philentropy::getDistMethods() for a list of supported methods, and vignette("tuning_and_metrics", package = "tidyclust") for usage examples.

Value

A tibble with two columns, the cluster name and the SSE within that cluster.

Details

sse_within_total() is the corresponding cluster metric function that returns the sum of the values given by sse_within().

Examples

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

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

sse_within(kmeans_fit)
#> # A tibble: 5 × 3
#>   .cluster     wss n_members
#>   <fct>      <dbl>     <int>
#> 1 Cluster_1  7256.         6
#> 2 Cluster_2  3617.         7
#> 3 Cluster_3  6356.         6
#> 4 Cluster_4 46659.         9
#> 5 Cluster_5   208.         4