visualpred: Visualization 2D of Binary Classification Models

Visual 2D point and contour plots for binary classification modeling under algorithms such as glm(), randomForest(), gbm(), nnet() and svm(), presented over two dimensions generated by FAMD and MCA methods. Package 'FactoMineR' for multivariate reduction functions and package 'MBA' for interpolation functions are used. The package can be used to visualize the discriminant power of input variables and algorithmic modeling, explore outliers, compare algorithm behaviour, etc. It has been created initially for teaching purposes, but it has also many practical uses.

Version: 0.1.0
Depends: R (≥ 3.5.0)
Imports: gbm, randomForest, nnet (≥ 7.3.12), e1071, MASS (≥ 7.3.51.4), magrittr, FactoMineR (≥ 2.3), ggplot2 (≥ 3.3.0), mltools, dplyr, data.table, MBA, pROC, ggrepel
Suggests: knitr, markdown, egg
Published: 2020-10-24
Author: Javier Portela [aut, cre]
Maintainer: Javier Portela <javipgm at gmail.com>
License: GPL (≥ 3)
NeedsCompilation: no
CRAN checks: visualpred results

Downloads:

Reference manual: visualpred.pdf
Vignettes: Advanced settings
visualpred package
Comparing algorithms
Plotting outliers
Package source: visualpred_0.1.0.tar.gz
Windows binaries: r-devel: visualpred_0.1.0.zip, r-release: visualpred_0.1.0.zip, r-oldrel: visualpred_0.1.0.zip
macOS binaries: r-release: visualpred_0.1.0.tgz, r-oldrel: visualpred_0.1.0.tgz

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