The aim of the precrec
package is to provide an integrated platform that enables robust performance evaluations of binary classifiers. Specifically, precrec
offers accurate calculations of ROC (Receiver Operator Characteristics) and precision-recall curves. All the main calculations of precrec
are implemented with C++/Rcpp.
Package website – GitHub pages that contain all precrec documentation.
Introduction to precrec – a package vignette that contains the descriptions of the functions with several useful examples. View the vignette with vignette("introduction", package = "precrec")
in R. The HTML version is also available on the GitHub Pages.
Help pages – all the functions including the S3 generics except for print
have their own help pages with plenty of examples. View the main help page with help(package = "precrec")
in R. The HTML version is also available on the GitHub Pages.
precrec
provides accurate precision-recall curves.
precrec
also calculates AUC scores with high accuracy.
precrec
calculates curves in a matter of seconds even for a fairly large dataset. It is much faster than most other tools that calculate ROC and precision-recall curves.
In addition to precision-recall and ROC curves, precrec
offers basic evaluation measures.
precrec
calculates confidence intervals when multiple test sets are given. It automatically shows confidence bands about the averaged curve in the corresponding plot.
Install the release version of precrec
from CRAN with install.packages("precrec")
.
Alternatively, you can install a development version of precrec
from our GitHub repository. To install it:
Make sure you have a working development environment.
Install devtools
from CRAN with install.packages("devtools")
.
Install precrec
from the GitHub repository with devtools::install_github("evalclass/precrec")
.
The precrec
package provides the following six functions.
Function | Description |
---|---|
evalmod | Main function to calculate evaluation measures |
mmdata | Reformat input data for performance evaluation calculation |
join_scores | Join scores of multiple models into a list |
join_labels | Join observed labels of multiple test datasets into a list |
create_sim_samples | Create random samples for simulations |
format_nfold | Create n-fold cross validation dataset from data frame |
Moreover, the precrec
package provides nine S3 generics for the S3 object created by the evalmod
function. N.B. The R language specifies S3 objects and S3 generic functions as part of the most basic object-oriented system in R.
S3 generic | Package | Description |
---|---|---|
base | Print the calculation results and the summary of the test data | |
as.data.frame | base | Convert a precrec object to a data frame |
plot | graphics | Plot performance evaluation measures |
autoplot | ggplot2 | Plot performance evaluation measures with ggplot2 |
fortify | ggplot2 | Prepare a data frame for ggplot2 |
auc | precrec | Make a data frame with AUC scores |
part | precrec | Calculate partial curves and partial AUC scores |
pauc | precrec | Make a data frame with pAUC scores |
auc_ci | precrec | Calculate confidence intervals of AUC scores |
Following two examples show the basic usage of precrec
functions.
Precrec: fast and accurate precision-recall and ROC curve calculations in R
Takaya Saito; Marc Rehmsmeier
Bioinformatics 2017; 33 (1): 145-147.
Classifier evaluation with imbalanced datasets - our web site that contains several pages with useful tips for performance evaluation on binary classifiers.
The Precision-Recall Plot Is More Informative than the ROC Plot When Evaluating Binary Classifiers on Imbalanced Datasets - our paper that summarized potential pitfalls of ROC plots with imbalanced datasets and advantages of using precision-recall plots instead.