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 244 downloads 1 exports 1 dependencies

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

TargetResultTimeFilesSyslog
linux-develOK98
source / vignettesOK164
linux-releaseOK89
macos-releaseOK134
macos-oldrelOK176
windows-develOK49
windows-releaseOK54
windows-oldrelOK60
wasm-releaseOK74

Exports:OKNNE

Dependencies:FNN