butcher (version 0.3.5)

axe-kknn: Axing an kknn.

Description

kknn objects are created from the kknn package, which is utilized to do weighted k-Nearest Neighbors for classification, regression and clustering.

Usage

# S3 method for kknn
axe_call(x, verbose = FALSE, ...)
# S3 method for kknn
axe_env(x, verbose = FALSE, ...)
# S3 method for kknn
axe_fitted(x, verbose = FALSE, ...)

Value

Axed kknn object.

Arguments

x

A model object.

verbose

Print information each time an axe method is executed. Notes how much memory is released and what functions are disabled. Default is FALSE.

...

Any additional arguments related to axing.

Examples

if (FALSE) { # rlang::is_installed(c("parsnip", "rsample", "rpart", "kknn"))
# Load libraries
library(parsnip)
library(rsample)
library(rpart)
library(kknn)

# Load data
set.seed(1234)
split <- initial_split(kyphosis, prop = 9/10)
spine_train <- training(split)

# Create model and fit
kknn_fit <- nearest_neighbor(mode = "classification",
                             neighbors = 3,
                             weight_func = "gaussian",
                             dist_power = 2) %>%
  set_engine("kknn") %>%
  fit(Kyphosis ~ ., data = spine_train)

out <- butcher(kknn_fit, verbose = TRUE)

# \donttest{
# Another kknn model object
m <- dim(iris)[1]
val <- sample(1:m,
              size = round(m/3),
              replace = FALSE,
              prob = rep(1/m, m))
iris.learn <- iris[-val,]
iris.valid <- iris[val,]
kknn_fit <- kknn(Species ~ .,
                 iris.learn,
                 iris.valid,
                 distance = 1,
                 kernel = "triangular")
out <- butcher(kknn_fit, verbose = TRUE)
# }
}