calibcurve's Introduction

calibcurve

calibcurve implements functions to compute true and predicted probabilities and visualise a calibration curve, aka reliability diagram (Niculescu-Mizil & Caruana, 2005).

calibcurve is powered by the yardstick’s infrastructure and its implementation of related curve metrics.
calibcurve borrowed some ideas from sklearn’s calibration module.

Installation

You can install the released version of calibcurve from CRAN with:

# install.packages("calibcurve")
# Not yet!

And the development version from GitHub with:

# install.packages("devtools")
devtools::install_github("chuvanan/calibcurve")

Example

This is a basic example which shows you how to solve a common problem:

library(calibcurve)
library(magrittr)
library(ggplot2)

data(two_class_example, package = "yardstick")
head(two_class_example)
#>    truth      Class1       Class2 predicted
#> 1 Class2 0.003589243 0.9964107574    Class2
#> 2 Class1 0.678621054 0.3213789460    Class1
#> 3 Class2 0.110893522 0.8891064779    Class2
#> 4 Class1 0.735161703 0.2648382969    Class1
#> 5 Class2 0.016239960 0.9837600397    Class2
#> 6 Class1 0.999275071 0.0007249286    Class1

two_class_example %>%
    calibration_curve(truth, Class1)
#> # A tibble: 10 x 2
#>    .mean_predicted .frac_positive
#>              <dbl>          <dbl>
#>  1        0.000626           0   
#>  2        0.0122             0.02
#>  3        0.0777             0.14
#>  4        0.213              0.26
#>  5        0.498              0.38
#>  6        0.762              0.56
#>  7        0.913              0.86
#>  8        0.978              0.98
#>  9        0.994              0.96
#> 10        0.999              1

two_class_example %>%
    calibration_curve(truth, Class1) %>%
    autoplot()

Code of Conduct

Please note that the calibcurve project is released with a Contributor Code of Conduct. By contributing to this project, you agree to abide by its terms.

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