View source: R/mltools_one_hot.R
one_hot | R Documentation |
One-Hot-Encode unordered factor columns of a data.table
one_hot(
dt,
cols = "auto",
sparsifyNAs = FALSE,
naCols = FALSE,
dropCols = TRUE,
dropUnusedLevels = FALSE
)
dt |
A data.table |
cols |
Which column(s) should be one-hot-encoded? DEFAULT = "auto" encodes all unordered factor columns |
sparsifyNAs |
Should NAs be converted to 0s? |
naCols |
Should columns be generated to indicate the present of NAs? Will only apply to factor columns with at least one NA |
dropCols |
Should the resulting data.table exclude the original columns which are one-hot-encoded? |
dropUnusedLevels |
Should columns of all 0s be generated for unused factor levels? |
One-hot-encoding converts an unordered categorical vector (i.e. a factor) to multiple binarized vectors where each binary vector of 1s and 0s indicates the presence of a class (i.e. level) of the of the original vector.
data.table object From the input data, a data frame in which categorical variables have been one-hot encoded is returned.
https://cran.r-project.org/web/packages/mltools
library(data.table)
dt <- data.table(
ID = 1:4,
color = factor(c("red", NA, "blue", "blue"), levels=c("blue", "green", "red"))
)
one_hot(dt)
one_hot(dt, sparsifyNAs=TRUE)
one_hot(dt, naCols=TRUE)
one_hot(dt, dropCols=FALSE)
one_hot(dt, dropUnusedLevels=TRUE)
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