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.
precrec
calculates partial AUCs for specified x and y ranges. It can
also draw partial ROC and precision-recall curves for the specified
ranges.
precrec
provides several useful functions that lack in most other
evaluation tools.
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 | |:--------------|:---------|:---------------------------------------------------------------| | 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 precrec
functions.
The 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)
The autoplot
function outputs ROC and Precision-Recall curves by using
the ggplot2
package.
# 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.
doi: 10.1093/bioinformatics/btw570
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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