distanceToCenter: Computes unsupervised features on numerical and integer...

In XanderHorn/autoML: Automated machine learning

Description

K-means clustering is used to create new features on existing numeric and integer features, then calculating the distance to center and using this as the new feature

Usage

distanceToCenter(x, numFeats, clusters = 10, autoCode = TRUE,
  progress = FALSE, seed = 1234)

Arguments

x	[data.frame | Required] Data.frame containing numeric features to transform
numFeats	[character vector | Required] Character vector of numerical features
clusters	[integer, Optional] Number of clusters to create
autoCode	[logical | Optional] Should code be generated when running the function
progress	[logical | Optional] Should a progress bar display the progress when running the function
seed	[integer | Optional] Random seed number for reproducable results. Default of 1991

Value

List containing data.frame with clustered features as well as code when autoCode is TRUE

Author(s)

Xander Horn

Examples

clst <- distanceToCenter(x = iris, numFeats = names(iris)[1:4])

XanderHorn/autoML documentation built on Aug. 5, 2020, 11:45 a.m.