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.
Six key features of precrec
1. Accurate curve calculations
precrec provides accurate precision-recall curves.
- Non-linear interpolation
- Elongation to the y-axis to estimate the first point when necessary
- Use of score-wise threshold values instead of fixed bins
precrec also calculates AUC scores with high accuracy.
2. Super fast
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.
3. Various evaluation metrics
In addition to precision-recall and ROC curves,
precrec offers basic evaluation measures.
- Error rate
- Sensitivity, true positive rate (TPR), recall
- Precision, positive predictive value (PPV)
- Matthews correlation coefficient
4. Confidence interval band
precrec calculates confidence intervals when multiple test sets are given. It automatically shows confidence bands about the averaged curve in the corresponding plot.
5. Calculation of partial AUCs and visualization of partial curves
precrec calculates partial AUCs for specified x and y ranges. It can also draw partial ROC and precision-recall curves for the specified ranges.
6. Supporting functions
precrec provides several useful functions that lack in most other evaluation tools.
- Handling multiple models and multiple test sets
- Handling tied scores and missing scores
- Pre- and post-process functions of simple data preparation and curve analysis
Install the release version of
precrecfrom CRAN with
Alternatively, you can install a development version of
precrecfrom our GitHub repository. To install it:
Make sure you have a working development environment.
- Windows: Install Rtools (available on the CRAN website).
- Mac: Install Xcode from the Mac App Store.
- Linux: Install a compiler and various development libraries (details vary across different flavors of Linux).
devtoolsfrom CRAN with
precrecfrom the GitHub repository with
precrec package provides the following six functions.
|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 eight 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.
|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|
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
help(package = "precrec")in R. The HTML version is also available on the GitHub Pages.
Following two examples show the basic usage of
ROC and Precision-Recall calculations
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)
Visualization of the curves
autoplot function outputs ROC and Precision-Recall curves by using the
# 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.