Package: visualpred 0.1.2

Javier Portela

visualpred: Visualization 2D of Binary Classification Models

Visual contour and 2D point and contour plots for binary classification modeling under algorithms such as 'glm', 'rf', '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 under the 'XAI' paradigm.

Authors:Javier Portela [aut, cre]

visualpred_0.1.2.tar.gz
visualpred_0.1.2.tar.gz(r-4.7-any)visualpred_0.1.2.tar.gz(r-4.6-any)
visualpred_0.1.2.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
visualpred/json (API)

# Install 'visualpred' in R:
install.packages('visualpred', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org'))
Datasets:
  • breastwisconsin1 - Breast Cancer Winsconsin dataset
  • Hmda - Home Mortgage Disclosure Act dataset
  • nba - Nba dataset
  • pima - Pima indian diabetes dataset
  • spiral - Spiral sample data

On CRAN:

Conda:

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

2.90 score 20 scripts 219 downloads 5 exports 119 dependencies

Last updated from:aa1545adf7. Checks:4 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-x86_64OK200
source / vignettesOK294
linux-release-x86_64OK191
wasm-releaseOK161

Exports:famdcontourfamdcontourlabelmcacontourmcacontourjitmcamodelobis

Dependencies:abindbackportsbase64encBHbootbroombslibcachemcarcarDataclasscliclustercolorspacecowplotcpp11crosstalkdata.tableDerivdigestdoBydplyrDTe1071ellipseemmeansestimabilityevaluateFactoMineRfarverfastmapflashClustfontawesomeforecastFormulafracdifffsgbmgenericsggplot2ggrepelgluegtablehighrhtmltoolshtmlwidgetsirlbaisobandjquerylibjsonliteknitrlabelinglaterlatticelazyevalleapslifecyclelme4lmtestmagrittrMASSMatrixMatrixModelsMBAmemoisemgcvmicrobenchmarkmimeminqamltoolsmodelrmultcompViewmvtnormnlmenloptrnnetnumDerivotelpbkrtestpillarpkgconfigpROCpromisesproxypurrrquantregR6randomForestrappdirsrbibutilsRColorBrewerRcppRcppArmadilloRcppEigenRdpackreformulasrlangrmarkdownS7sassscalesscatterplot3dSparseMstringistringrsurvivaltibbletidyrtidyselecttimeDatetinytexurcautf8vctrsviridisLitewithrxfunyamlzoo

1-visualpred package
Basic example with FAMD | Differences between mcacontour and famdcontour | Dataset with both input class and interval variables
Rendered fromBasic_example.Rmdusingknitr::knitr

Last update: 2025-10-19
Started: 2020-10-24

2-Comparing algorithms
Comparing algorithms | Tuning representation | Real dataset fitting under different algorithms
Rendered fromComparing.Rmdusingknitr::knitr

Last update: 2025-10-19
Started: 2020-10-24

3-Plotting outliers
Outliers with respect to plot dimensions | Outliers with respect to model fit
Rendered fromOutliers.Rmdusingknitr::knitr

Last update: 2025-10-19
Started: 2020-10-24

4-Advanced settings
Using external predictions | Colors and titles | Plotting over selected dimensions
Rendered fromAdvanced.Rmdusingknitr::knitr

Last update: 2025-10-19
Started: 2020-10-24