Package: visualpred 0.1.1
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:
visualpred_0.1.1.tar.gz
visualpred_0.1.1.tar.gz(r-4.5-noble)visualpred_0.1.1.tar.gz(r-4.4-noble)
visualpred_0.1.1.tgz(r-4.4-emscripten)visualpred_0.1.1.tgz(r-4.3-emscripten)
visualpred.pdf |visualpred.html✨
visualpred/json (API)
NEWS
# Install 'visualpred' in R: |
install.packages('visualpred', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org')) |
- Hmda - Home Mortgage Disclosure Act dataset
- breastwisconsin1 - Breast Cancer Winsconsin dataset
- nba - Nba dataset
- pima - Pima indian diabetes dataset
- spiral - Spiral sample data
This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.
Last updated 15 days agofrom:e65a36e3e1. Checks:OK: 2. Indexed: yes.
Target | Result | Date |
---|---|---|
Doc / Vignettes | OK | Nov 08 2024 |
R-4.5-linux | OK | Nov 08 2024 |
Exports:famdcontourfamdcontourlabelmcacontourmcacontourjitmcamodelobis
Dependencies:abindbackportsbase64encBHbootbroombslibcachemcarcarDataclasscliclustercolorspacecowplotcpp11crosstalkdata.tableDerivdigestdoBydplyrDTe1071ellipseemmeansestimabilityevaluateFactoMineRfansifarverfastmapflashClustfontawesomeFormulafsgbmgenericsggplot2ggrepelgluegtablehighrhtmltoolshtmlwidgetshttpuvisobandjquerylibjsonliteknitrlabelinglaterlatticelazyevalleapslifecyclelme4magrittrMASSMatrixMatrixModelsMBAmemoisemgcvmicrobenchmarkmimeminqamltoolsmodelrmultcompViewmunsellmvtnormnlmenloptrnnetnumDerivpbkrtestpillarpkgconfigplyrpROCpromisesproxypurrrquantregR6randomForestrappdirsRColorBrewerRcppRcppEigenrlangrmarkdownsassscalesscatterplot3dSparseMstringistringrsurvivaltibbletidyrtidyselecttinytexutf8vctrsviridisLitewithrxfunyaml
Advanced settings
Rendered fromAdvanced.Rmd
usingknitr::knitr
on Nov 08 2024.Last update: 2020-10-24
Started: 2020-10-24
Comparing algorithms
Rendered fromComparing.Rmd
usingknitr::knitr
on Nov 08 2024.Last update: 2020-10-24
Started: 2020-10-24
Plotting outliers
Rendered fromOutliers.Rmd
usingknitr::knitr
on Nov 08 2024.Last update: 2020-10-24
Started: 2020-10-24
visualpred package
Rendered fromBasic_example.Rmd
usingknitr::knitr
on Nov 08 2024.Last update: 2020-10-24
Started: 2020-10-24
Readme and manuals
Help Manual
Help page | Topics |
---|---|
Breast Cancer Winsconsin dataset | breastwisconsin1 |
Contour plots and FAMD function for classification modeling | famdcontour |
Outliers in Contour plots and FAMD function for classification modeling | famdcontourlabel |
Home Mortgage Disclosure Act dataset | Hmda |
Contour plots and MCA function for classification modeling | mcacontour |
Contour plots and MCA function for classification modeling | mcacontourjit |
Basic MCA function for clasification | mcamodelobis |
nba dataset | nba |
Pima indian diabetes dataset | pima |
spiral sample data | spiral |