Package: OkNNE 1.0.1

OkNNE: A k-Nearest Neighbours Ensemble via Optimal Model Selection for Regression

Optimal k Nearest Neighbours Ensemble is an ensemble of base k nearest neighbour models each constructed on a bootstrap sample with a random subset of features. k closest observations are identified for a test point "x" (say), in each base k nearest neighbour model to fit a stepwise regression to predict the output value of "x". The final predicted value of "x" is the mean of estimates given by all the models. The implemented model takes training and test datasets and trains the model on training data to predict the test data. Ali, A., Hamraz, M., Kumam, P., Khan, D.M., Khalil, U., Sulaiman, M. and Khan, Z. (2020) <doi:10.1109/ACCESS.2020.3010099>.

Authors:Amjad Ali [aut, cre, cph], Zardad Khan [aut, ths], Muhammad Hamraz [aut]

OkNNE_1.0.1.tar.gz
OkNNE_1.0.1.zip(r-4.7-any)OkNNE_1.0.1.zip(r-4.6-any)OkNNE_1.0.1.zip(r-4.5-any)
OkNNE_1.0.1.tgz(r-4.6-any)OkNNE_1.0.1.tgz(r-4.5-any)
OkNNE_1.0.1.tar.gz(r-4.7-any)OkNNE_1.0.1.tar.gz(r-4.6-any)
OkNNE_1.0.1.tgz(r-4.6-emscripten)
manual.pdf |manual.html
DESCRIPTION
card.svg |card.png
OkNNE/json (API)

# Install 'OkNNE' in R:
install.packages('OkNNE', repos = c('https://amjadali0313.r-universe.dev', 'https://cloud.r-project.org'))
Datasets:
  • SMSA - Standard Metropolitan Statistical Areas

On CRAN:

Conda:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

1.00 score 240 downloads 1 exports 1 dependencies

Last updated from:2473f791d8. Checks:9 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-x86_64OK111
source / vignettesOK171
linux-release-x86_64OK125
macos-release-arm64OK201
macos-oldrel-arm64OK174
windows-develOK101
windows-releaseOK68
windows-oldrelOK62
wasm-releaseOK89

Exports:OKNNE

Dependencies:FNN