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:
OkNNE_1.0.1.tar.gz
OkNNE_1.0.1.zip(r-4.5)OkNNE_1.0.1.zip(r-4.4)OkNNE_1.0.1.zip(r-4.3)
OkNNE_1.0.1.tgz(r-4.4-any)OkNNE_1.0.1.tgz(r-4.3-any)
OkNNE_1.0.1.tar.gz(r-4.5-noble)OkNNE_1.0.1.tar.gz(r-4.4-noble)
OkNNE_1.0.1.tgz(r-4.4-emscripten)OkNNE_1.0.1.tgz(r-4.3-emscripten)
OkNNE.pdf |OkNNE.html✨
OkNNE/json (API)
# Install 'OkNNE' in R: |
install.packages('OkNNE', repos = c('https://amjadali0313.r-universe.dev', 'https://cloud.r-project.org')) |
- SMSA - Standard Metropolitan Statistical Areas
This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.
Last updated 2 years agofrom:2473f791d8. Checks:OK: 7. Indexed: yes.
Target | Result | Date |
---|---|---|
Doc / Vignettes | OK | Nov 10 2024 |
R-4.5-win | OK | Nov 10 2024 |
R-4.5-linux | OK | Nov 10 2024 |
R-4.4-win | OK | Nov 10 2024 |
R-4.4-mac | OK | Nov 10 2024 |
R-4.3-win | OK | Nov 10 2024 |
R-4.3-mac | OK | Nov 10 2024 |
Exports:OKNNE
Dependencies:FNN
Readme and manuals
Help Manual
Help page | Topics |
---|---|
A k-Nearest Neighbours Ensemble via Optimal Model Selection for Regression | OkNNE-package OkNNE |
Optimal k-Nearest Neighbours Ensemble | OKNNE |
Standard Metropolitan Statistical Areas | SMSA |