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OkNNE  

A k-Nearest Neighbours Ensemble via Optimal Model Selection for Regression
View on CRAN: Click here


Download and install OkNNE package within the R console
Install from CRAN:
install.packages("OkNNE")

Install from Github:
library("remotes")
install_github("cran/OkNNE")

Install by package version:
library("remotes")
install_version("OkNNE", "1.0.1")



Attach the package and use:
library("OkNNE")
Maintained by
Amjad Ali
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2020-08-13
Latest Update: 2022-12-19
Description:
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) .
How to cite:
Amjad Ali (2020). OkNNE: A k-Nearest Neighbours Ensemble via Optimal Model Selection for Regression. R package version 1.0.1, https://cran.r-project.org/web/packages/OkNNE. Accessed 22 Dec. 2024.
Previous versions and publish date:
1.0.0 (2020-08-13 10:20)
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Complete documentation for OkNNE
Functions, R codes and Examples using the OkNNE R package
Some associated functions: OKNNE . OkNNE-package . SMSA . 
Some associated R codes: OKNNE.R .  Full OkNNE package functions and examples
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