Package: classifierplots 1.4.0

classifierplots: Generates a Visualization of Classifier Performance as a Grid of Diagnostic Plots

Generates a visualization of binary classifier performance as a grid of diagnostic plots with just one function call. Includes ROC curves, prediction density, accuracy, precision, recall and calibration plots, all using ggplot2 for easy modification. Debug your binary classifiers faster and easier!

Authors:Aaron Defazio [aut, cre], Huw Campbell [aut]

classifierplots_1.4.0.tar.gz
classifierplots_1.4.0.zip(r-4.7-any)classifierplots_1.4.0.zip(r-4.6-any)classifierplots_1.4.0.zip(r-4.5-any)
classifierplots_1.4.0.tgz(r-4.6-any)classifierplots_1.4.0.tgz(r-4.5-any)
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classifierplots_1.4.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION
card.svg |card.png
classifierplots/json (API)

# Install 'classifierplots' in R:

install.packages('classifierplots', repos = c('https://adefazio.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/adefazio/classifierplots/issues

Datasets:

example_predictions - Generated using the gen_example included in the github source

On CRAN:

5.26 score 49 stars 25 scripts 433 downloads 1 mentions 13 exports 80 dependencies

Last updated from:6d2484899c. Checks:7 NOTE, 2 OK. Indexed: yes.

Target	Result	Time
linux-devel	NOTE	164
source / vignettes	OK	191
linux-release	NOTE	146
macos-release	NOTE	216
macos-oldrel	NOTE	220
windows-devel	NOTE	100
windows-release	NOTE	111
windows-oldrel	NOTE	110
wasm-release	OK	124

Exports:accuracy_plot calibration_plot classifierplots classifierplots_folder density_plot lift_plot notation_key_plot positives_plot precision_plot propensity_plot recall_plot roc_plot sigmoid

Dependencies:bitops caret caTools class cli clock codetools cpp11 data.table diagram digest dplyr e1071 farver foreach future future.apply generics ggplot2 globals glue gower gplots gridExtra gtable gtools hardhat ipred isoband iterators KernSmooth labeling lattice lava lifecycle listenv lubridate magrittr MASS Matrix ModelMetrics nlme nnet numDeriv parallelly pillar pkgconfig plyr png pROC prodlim progressr proxy purrr R6 RColorBrewer Rcpp recipes reshape2 rlang ROCR rpart S7 scales shape sparsevctrs SQUAREM stringi stringr survival tibble tidyr tidyselect timechange timeDate tzdb utf8 vctrs viridisLite withr

Citation

Development and contributors

Readme and manuals

Help Manual

Help page	Topics
accuracy_plot	accuracy_plot
calculate_auc	calculate_auc
calibration_plot	calibration_plot
The main functions you want are 'classifierplots' or 'classifierplots_folder'.	classifierplots-package classifierplots
classifierplots_folder	classifierplots_folder
density_plot	density_plot
Generated using the gen_example included in the github source	example_predictions
lift_plot	lift_plot
notation_key_plot	notation_key_plot
positives_plot	positives_plot
precision_plot	precision_plot
propensity_plot	propensity_plot
recall_plot	recall_plot
roc_plot	roc_plot
sigmoid	sigmoid