scikit-plot
An intuitive library to add plotting functionality to scikit-learn objects.
Decision gist · record as of 2026-08-14
Yes, if you need fast, minimal-boilerplate ML evaluation plots and can tolerate dormant maintenance. The low install friction and permissive license make it a low-risk addition to analysis workflows. However, be aware that no active development means no fixes for compatibility issues with newer dependencies—test thoroughly in your environment before relying on it in production pipelines.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Low install friction with standard dependencies (matplotlib, scikit-learn, scipy, joblib).
- Package is dormant—last release was 2018-08-19 and no commits since 2024-08-20—so expect no active maintenance or bug fixes.
License · maintenance · safety
MIT License (permissive) — MIT License (permissive) allows free use, modification, and distribution with minimal restrictions, making it suitable for both open-source and commercial projects.
last release 2018-08-19 (2917 days) · last repo commit 2024-08-20 · 2,433 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 511,194 downloads/mo, #6,261 on PyPI
Alternatives
Verify before relying
pip install scikit-plot
import scikitplot as skplt
import matplotlib.pyplot as plt
# After training a classifier and generating predicted_probas:
skplt.metrics.plot_roc(y_test, predicted_probas)
plt.show()- Whether the package works reliably with modern versions of matplotlib and scikit-learn given dormant maintenance since 2018
- Whether Python 2.7 and 3.5 support listed in classifiers remains functional or is historical only
- Compatibility with current versions of scipy and joblib
What it is and what it does
Scikit-plot is a thin wrapper around matplotlib that generates common machine learning evaluation visualizations—ROC curves, confusion matrices, precision-recall plots, and similar metrics—with minimal boilerplate. Instead of writing dozens of lines to configure matplotlib and compute multi-class variants, you call a single function with your ground-truth labels and predicted probabilities, and get a polished, publication-ready plot.
The library works with any classifier that produces probability predictions. It depends on matplotlib for rendering, scikit-learn for metric computations, scipy for statistical operations, and joblib for parallelization. The package has been dormant since 2018-08-19, so it receives no active maintenance or updates.
Use it for
- Quickly visualize multi-class ROC curves with micro and macro averages for classifier evaluation
- Generate confusion matrices with class labels for model diagnostic reports
- Plot precision-recall curves for imbalanced classification problems
- Create publication-ready evaluation plots for academic papers or presentations without manual matplotlib configuration
- Compare classifier performance across multiple metrics in a single visualization pass
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, if you need fast, minimal-boilerplate ML evaluation plots and can tolerate dormant maintenance.
The low install friction and permissive license make it a low-risk addition to analysis workflows. However, be aware that no active development means no fixes for compatibility issues with newer dependencies—test thoroughly in your environment before relying on it in production pipelines.
Install
scikit-plot on PyPI
Before you install
Low install friction with standard dependencies (matplotlib, scikit-learn, scipy, joblib). Package is dormant—last release was 2018-08-19 and no commits since 2024-08-20—so expect no active maintenance or bug fixes.
License in practice
MIT License (permissive) allows free use, modification, and distribution with minimal restrictions, making it suitable for both open-source and commercial projects.
Quickstart
pip install scikit-plot
import scikitplot as skplt
import matplotlib.pyplot as plt
# After training a classifier and generating predicted_probas:
skplt.metrics.plot_roc(y_test, predicted_probas)
plt.show()Verify before relying
- Whether the package works reliably with modern versions of matplotlib and scikit-learn given dormant maintenance since 2018
- Whether Python 2.7 and 3.5 support listed in classifiers remains functional or is historical only
- Compatibility with current versions of scipy and joblib
Package facts
| License | MIT License permissive |
| Python support | Not specified |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 4 packagesmatplotlibscikit-learnscipyjoblib |
| Maintenance | Dormant 2,917 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 511,194 / month, #6,261 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Intended Audience :: DevelopersIntended Audience :: Science/ResearchLicense :: OSI Approved :: MIT LicenseNatural Language :: EnglishOperating System :: OS IndependentProgramming Language :: PythonProgramming Language :: Python :: 2Programming Language :: Python :: 2.7Programming Language :: Python :: 3Programming Language :: Python :: 3.5Programming Language :: Python :: 3.6Topic :: Scientific/Engineering :: Visualization |
Evidence: scikit_plot-0.3.7-py3-none-any.whl
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