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Compute the ratio of the WSS to the total SSE
sse_ratio(object, ...)# S3 method for cluster_spec sse_ratio(object, ...)# S3 method for cluster_fit sse_ratio(object, new_data = NULL, dist_fun = NULL, ...)# S3 method for workflow sse_ratio(object, new_data = NULL, dist_fun = NULL, ...)sse_ratio_vec( object, new_data = NULL, dist_fun = function(x, y) { philentropy::dist_many_many(x, y, method = "euclidean") }, ... )
# S3 method for cluster_spec sse_ratio(object, ...)
# S3 method for cluster_fit sse_ratio(object, new_data = NULL, dist_fun = NULL, ...)
# S3 method for workflow sse_ratio(object, new_data = NULL, dist_fun = NULL, ...)
sse_ratio_vec( object, new_data = NULL, dist_fun = function(x, y) { philentropy::dist_many_many(x, y, method = "euclidean") }, ... )
A tibble with 3 columns; .metric, .estimator, and .estimate.
.metric
.estimator
.estimate
A fitted kmeans tidyclust model
Other arguments passed to methods.
A dataset to predict on. If NULL, uses trained clustering.
NULL
A function for calculating distances to centroids. Defaults to Euclidean distance on processed data.
Other cluster metric: silhouette_avg(), sse_total(), sse_within_total()
silhouette_avg()
sse_total()
sse_within_total()
kmeans_spec <- k_means(num_clusters = 5) %>% set_engine("stats") kmeans_fit <- fit(kmeans_spec, ~., mtcars) sse_ratio(kmeans_fit) sse_ratio_vec(kmeans_fit)
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