---
id: scikit-plot
version: "0.3.7"
license: MIT License
license_treatment: permissive
maintenance: dormant
---

# scikit-plot — An intuitive library to add plotting functionality to scikit-learn objects.

License: permissive · Maintenance: dormant · Downloads: 511.2K/mo

## 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 above — verify before relying.

Scikit-plot generates publication-ready visualizations for machine learning evaluation metrics with single-line function calls, working with scikit-learn and other classifiers.

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

pip install scikit-plot
uv add scikit-plot
poetry add scikit-plot

## Installing scikit-plot

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: unspecified
- Install friction: low
- Maintenance: dormant
- Downloads: 511.2K/month (top 15,000 on PyPI)
- Known vulnerabilities: none known

## Tags

machine learning visualization, ROC curve plotting, confusion matrix visualization, classifier evaluation plots, metrics visualization, precision recall curves, classification metrics charts, machine-learning-viz, classifier-metrics, matplotlib-wrapper

[View on SkillFed](https://skillfed.io/packages/scikit-plot) · [View on PyPI](https://pypi.org/project/scikit-plot/)