prcbench: prcbench: A package to provide a testing workbench for...
In prcbench: Testing Workbench for Precision-Recall Curves
prcbench R Documentation
prcbench: A package to provide a testing workbench for
precision-recall curves
Description
The prcbench package provides four categories of important functions:
tool interface, test data interface, benchmarking, and curve evaluation.
Tool interface
The create_toolset function creates a common interface for
seven different tools that calculate Precision-Recall curves. These tools
are ROCR,
AUCCalculator,
PerfMeas,
PRROC,
precrec,
yardstick, and
scikit-learn.
The sklearn tool is calculated by a standalone Python module that is
bundled with prcbench and derived from the scikit-learn source
code. It requires reticulate, a working Python installation and
numpy. Without them it returns a flat dummy curve rather than
raising an error, so the predefined tool sets that contain it stay
usable.
The create_usrtool function helps users to make the same
interface of the predefined ones for their own tools.
Test data interface
The create_testset function creates two different types of test
data sets. The first type is for benchmarking, and the second type is for
curve evaluation.
The create_usrdata function helps users to make their own test
data sets.
Benchmarking
The run_benchmark function takes a tool set and a test data set
and run microbenchmark for them.
The timing of the sklearn tool includes the cost of crossing the
R/Python boundary, so it is not comparable with the timings of the tools
written in R.
Curve evaluation
The run_evalcurve function takes a tool set and a test data set
and evaluates the accuracy of Precision-Recall curves for them.
Author(s)
Maintainer: Takaya Saito takaya.saito@outlook.com (ORCID)
Authors:
Takaya Saito takaya.saito@outlook.com (ORCID)
Marc Rehmsmeier marc.rehmsmeier@ii.uib.no (ORCID)
Other contributors:
The scikit-learn developers (Python code in inst/python, derived from scikit-learn (BSD-3-Clause); see inst/COPYRIGHTS) [copyright holder]
See Also
Useful links:
prcbench documentation built on Sept. 26, 2026, 5:06 p.m.
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| prcbench | R Documentation |
prcbench: A package to provide a testing workbench for precision-recall curves
Description
The prcbench package provides four categories of important functions: tool interface, test data interface, benchmarking, and curve evaluation.
Tool interface
The create_toolset function creates a common interface for
seven different tools that calculate Precision-Recall curves. These tools
are ROCR,
AUCCalculator,
PerfMeas,
PRROC,
precrec,
yardstick, and
scikit-learn.
The sklearn tool is calculated by a standalone Python module that is
bundled with prcbench and derived from the scikit-learn source
code. It requires reticulate, a working Python installation and
numpy. Without them it returns a flat dummy curve rather than
raising an error, so the predefined tool sets that contain it stay
usable.
The create_usrtool function helps users to make the same
interface of the predefined ones for their own tools.
Test data interface
The create_testset function creates two different types of test
data sets. The first type is for benchmarking, and the second type is for
curve evaluation.
The create_usrdata function helps users to make their own test
data sets.
Benchmarking
The run_benchmark function takes a tool set and a test data set
and run microbenchmark for them.
The timing of the sklearn tool includes the cost of crossing the
R/Python boundary, so it is not comparable with the timings of the tools
written in R.
Curve evaluation
The run_evalcurve function takes a tool set and a test data set
and evaluates the accuracy of Precision-Recall curves for them.
Author(s)
Maintainer: Takaya Saito takaya.saito@outlook.com (ORCID)
Authors:
Takaya Saito takaya.saito@outlook.com (ORCID)
Marc Rehmsmeier marc.rehmsmeier@ii.uib.no (ORCID)
Other contributors:
The scikit-learn developers (Python code in inst/python, derived from scikit-learn (BSD-3-Clause); see inst/COPYRIGHTS) [copyright holder]
See Also
Useful links:
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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