auto_grouping.RdReduce the cardinality of an input variable based on a target -binary by now- variable based on attribitues of accuracy and representativity, for both input and target variable. It uses a cluster model to create the new groups. Full documentation can be found at: https://livebook.datascienceheroes.com/data-preparation.html#high_cardinality_predictive_modeling
auto_grouping(data, input, target, n_groups, model = "kmeans", seed = 999)
| data | data frame source |
|---|---|
| input | categorical variable indicating |
| target | string of the variable to optimize the re-grouping |
| n_groups | number of groups for the new category based on input, normally between 3 and 10. |
| model | is the clustering model used to create the grouping, supported models: "kmeans" (default) or "hclust" (hierarchical clustering). |
| seed | optional, random number used internally for the k-means, changing this value will change the model |
A list containing 3 elements: recateg_results which contains the description of the target variable with the new groups; df_equivalence is a data frame containing the input category and the new category; fit_cluster which is the cluster model used to do the re-grouping
if (FALSE) { # Reducing quantity of countries based on has_flu variable auto_grouping(data=data_country, input='country', target="has_flu", n_groups=8) }