[R-Package] mulset - generate dataset intersections in sparse data
Posted: Mon May 06, 2019 5:39 pm
Description
If you have missing values in your data, but you dont want to impute those values or mess with them in any other artificial way,
you can use mulset and generate multiple datasets of those data that you can use latter on in autoML or some other analysis.
Download links
Usage Example
If you have missing values in your data, but you dont want to impute those values or mess with them in any other artificial way,
you can use mulset and generate multiple datasets of those data that you can use latter on in autoML or some other analysis.
Download links
- https://cran.r-project.org/web/packages/mulset/index.html
- https://github.com/LogIN-/mulset
Code: Select all
install.packages("devtools")
devtools::install_github("LogIN-/mulset", subdir = 'R-package')
Code: Select all
library("mulset")
## Using provided demo data
data(mulsetDemo)
resamples <- mulset(mulsetDemo, exclude = c("outcome", "age", "gender"), 250)
## or from CSV file
## mulsetDemo <-fread("./data/mulsetDemo.csv", header = T, sep = ',', stringsAsFactors = FALSE, data.table = FALSE)
## exclude <- c("come_column")
## resamples <- mulset(mulsetDemo, exclude = exclude, include = c("samples_count", "datapoints"), maxIntersections = 250, hashMethod = "sha1")