hai_kmeans_automl: Automatic K-Means H2O

In healthyR.ai: The Machine Learning and AI Modeling Companion to 'healthyR'

Automatic K-Means H2O

Description

This is a wrapper around the h2o::h2o.kmeans() function that will return a list object with a lot of useful and easy to use tidy style information.

Usage

hai_kmeans_automl(
  .data,
  .split_ratio = 0.8,
  .seed = 1234,
  .centers = 10,
  .standardize = TRUE,
  .print_model_summary = TRUE,
  .predictors,
  .categorical_encoding = "auto",
  .initialization_mode = "Furthest",
  .max_iterations = 100
)

Arguments

.data	The data that is to be passed for clustering.
.split_ratio	The ratio for training and testing splits.
.seed	The default is 1234, but can be set to any integer.
.centers	The default is 1. Specify the number of clusters (groups of data) in a data set.
.standardize	The default is set to TRUE. When TRUE all numeric columns will be set to zero mean and unit variance.
.print_model_summary	This is a boolean and controls if the model summary is printed to the console. The default is TRUE.
.predictors	This must be in the form of c("column_1", "column_2", ... "column_n")
.categorical_encoding	Can be one of the following: "auto" "enum" "one_hot_explicit" "binary" "eigen" "label_encoder" "sort_by_response" "enum_limited"
.initialization_mode	This can be one of the following: "Random" "Furthest (default) "PlusPlus"
.max_iterations	The default is 100. This specifies the number of training iterations

Value

A list object

Author(s)

Steven P. Sanderson II, MPH

See Also

Other Kmeans: hai_kmeans_automl_predict(), hai_kmeans_mapped_tbl(), hai_kmeans_obj(), hai_kmeans_scree_data_tbl(), hai_kmeans_scree_plt(), hai_kmeans_tidy_tbl(), hai_kmeans_user_item_tbl()

Examples

## Not run: 
h2o.init()
output <- hai_kmeans_automl(
  .data = iris,
  .predictors = c("Sepal.Width", "Sepal.Length", "Petal.Width", "Petal.Length"),
  .standardize = FALSE
)
h2o.shutdown()

## End(Not run)

healthyR.ai documentation built on April 3, 2023, 5:24 p.m.