Package: Imneuron 0.1.0

Imneuron: AI Powered Neural Network Solutions for Regression Tasks

It offers a sophisticated and versatile tool for creating and evaluating artificial intelligence based neural network models tailored for regression analysis on datasets with continuous target variables. Leveraging the power of neural networks, it allows users to experiment with various hidden neuron configurations across two layers, optimizing model performance through "5 fold"" or "10 fold"" cross validation. The package normalizes input data to ensure efficient training and assesses model accuracy using key metrics such as R squared (R2), Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Percentage Error (PER). By storing and visualizing the best performing models, it provides a comprehensive solution for precise and efficient regression modeling making it an invaluable tool for data scientists and researchers aiming to harness AI for predictive analytics.

Authors:M Iqbal Jeelani [aut, cre], Fehim Jeelani Wani [aut]

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Imneuron.pdf |Imneuron.html
Imneuron/json (API)

# Install 'Imneuron' in R:
install.packages('Imneuron', repos = c('https://imiqbal.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Datasets:
  • fruit - Fruit Characteristics

On CRAN:

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

1.00 score 463 downloads 1 exports 37 dependencies

Last updated 5 months agofrom:902b0d63b7. Checks:OK: 7. Indexed: yes.

TargetResultDate
Doc / VignettesOKOct 31 2024
R-4.5-winOKOct 31 2024
R-4.5-linuxOKOct 31 2024
R-4.4-winOKOct 31 2024
R-4.4-macOKOct 31 2024
R-4.3-winOKOct 31 2024
R-4.3-macOKOct 31 2024

Exports:Imneuron

Dependencies:bitopscaToolsclicolorspaceDerivfansifarverggplot2gluegplotsgtablegtoolsisobandKernSmoothlabelinglatticelifecyclemagrittrMASSMatrixmgcvMLmetricsmunsellneuralnetnlmepillarpkgconfigR6RColorBrewerrlangROCRscalestibbleutf8vctrsviridisLitewithr