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, ...)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
# Load libraries
library(parsnip)
library(rsample)
library(rpart)
library(kknn)
#>
#> Attaching package: ‘kknn’
#> The following object is masked from ‘package:caret’:
#>
#> contr.dummy
# 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)
#> ✔ Memory released: 8.17 kB
# \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)
#> ✔ Memory released: 47.21 kB
#> ✖ Disabled: `print()`, `summary()`, and `fitted()`
# }