Yellowbrick: Visualize and compare machine-learning models

Understand the project

Practical overview

Use Yellowbrick when a scikit-learn workflow would benefit from visual diagnostics for feature inspection or model selection.

Python practitioners using scikit-learn who want visual diagnostics during model selection.
Data scientists and learners who want to inspect feature relationships or classifier tradeoffs with plots.

Compare features pairwise using a visualizer such as Rank2D.
View a classifier’s sensitivity-specificity tradeoff with the ROCAUC visualizer.
Add estimator visualizations to scikit-learn analysis workflows.

Before you choose

The public repository specifies an Apache-2.0 license. The README documents pip and Anaconda conda installation.

The library is built around the Python, scikit-learn and Matplotlib ecosystem.
The repository documents Python 3.4 or later and lists scikit-learn and Matplotlib as dependencies.
Its visualizers support human inspection and steering of model selection rather than automating the selection decision.

Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.

Source-based practical guide

Install Yellowbrick and its dependencies using one of the installation methods documented in the repository.

Use pip to install Yellowbrick with the command below.
Documented command
```
pip install yellowbrick
```
Alternatively, for an Anaconda environment, use conda install -c districtdatalabs yellowbrick.

Yellowbrick is installed with the dependencies needed to begin following its documented visualizer examples.

The repository documents installation, but this guide does not verify installation or execution in a particular environment.

Evidence and transparency

Source record details

Source availability: Active
Sources checked: 14 October 2026
Original website title: DistrictDataLabs/yellowbrick · Official homepage title · retained in its source language