svd_entropy: Feature importance based on data entropy.

In CellProfiler/cytominer: Methods for Image-Based Cell Profiling

Feature importance based on data entropy.

Description

svd_entropy measures the contribution of each feature in decreasing the data entropy.

Usage

svd_entropy(sample, variables, cores = NULL)

Arguments

sample	tbl containing sample used to estimate parameters.
variables	character vector specifying observation variables.
cores	optional integer specifying number of CPU cores used for parallel computing using doParallel.

Value

data frame specifying the contribution of each feature in decreasing the data entropy. Higher values indicate more information.

Examples

sample <- tibble::tibble(
  AreaShape_MinorAxisLength = c(10, 12, 15, 16, 8, 8, 7, 7, 13, 18),
  AreaShape_MajorAxisLength = c(35, 18, 22, 16, 9, 20, 11, 15, 18, 42),
  AreaShape_Area = c(245, 151, 231, 179, 50, 112, 53, 73, 164, 529)
)
variables <- c("AreaShape_MinorAxisLength", "AreaShape_MajorAxisLength", "AreaShape_Area")
svd_entropy(sample, variables, cores = 1)

CellProfiler/cytominer documentation built on July 2, 2023, 6:19 p.m.