The aim of the
precrec package is to provide an integrated platform
that enables robust performance evaluations of binary classifiers.
precrec offers accurate calculations of ROC (Receiver
Operator Characteristics) and precision-recall curves. All the main
precrec are implemented with
Package website – GitHub pages that contain all precrec documentation.
– 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
Help pages – all
the functions including the S3 generics except for
help(package = "precrec") in R. The HTML version is also
available on the GitHub
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
precrec calculates confidence intervals when multiple test sets are
given. It automatically shows confidence bands about the averaged curve
in the corresponding plot.
precrec calculates partial AUCs for specified x and y ranges. It can
also draw partial ROC and precision-recall curves for the specified
precrec provides several useful functions that lack in most other
Install the release version of
precrec from CRAN with
Alternatively, you can install a development version of
from our GitHub repository.
To install it:
Make sure you have a working development environment.
devtools from CRAN with
precrec from the GitHub repository with
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 |
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 | |:--------------|:---------|:---------------------------------------------------------------| | print | 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
evalmod function calculates ROC and Precision-Recall curves and
returns an S3 object.
library(precrec) # Load a test dataset data(P10N10) # Calculate ROC and Precision-Recall curves sscurves <- evalmod(scores = P10N10$scores, labels = P10N10$labels)
autoplot function outputs ROC and Precision-Recall curves by using
# The ggplot2 package is required library(ggplot2) # Show ROC and Precision-Recall plots autoplot(sscurves)
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.
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