Yellowbrick: Machine Learning Visualization¶
Yellowbrick extends the Scikit-Learn API to make model selection and hyperparameter tuning easier. Under the hood, it’s using Matplotlib.
Recommended Learning Path¶
- Check out the Quick Start
- Try the the Model Selection Tutorial.
- Use Yellowbrick in your project.
- For details on specific Visualizers and extended usage head over to the Visualizers and API.
- Follow us on Twitter @scikit_yb. You’ll hear about new visualizers as they’re added.
Contributing¶
Interested in contributing to Yellowbrick? Yellowbrick is a welcoming, inclusive project and we would love to have you. We follow the Python Software Foundation Code of Conduct.
No matter your level of technical skill, you can be helpful. We appreciate bug reports, user testing, feature requests, bug fixes, product enhancements, and documentation improvements.
Check out the Contributing guide!
If you’ve signed up to do user testing, head over to the User Testing Instructions.
Please consider joining the Google Groups Listserv listserve so you can respond to questions.
Thank you for your contributions!
Concepts & API¶
Visualizers¶
The primary goal of Yellowbrick is to create a sensical API similar to Scikit-Learn.
Visualizers are the core objects in Yellowbrick. They are similar to transformers in Scikit-Learn. Visualizers can wrap a model estimator - similar to how the “ModelCV” (e.g. RidgeCV, LassoCV) methods work.
Some of our most popular visualizers include:
Feature Visualization¶
- Rank Features: pairwise ranking of features to detect relationships
- Parallel Coordinates: horizontal visualization of instances
- Radial Visualization: separation of instances around a circular plot
- PCA Projection: projection of instances based on principal components
- Manifold Visualization: high dimensional visualization with manifold learning
- Feature Importances: rank features by importance or linear coefficients for a specific model
- Recursive Feature Elimination: find the best subset of features based on importance
- Joint Plots: direct data visualization with feature selection
Classification Visualization¶
- Class Prediction Error: shows error and support in classification
- Classification Report: visual representation of precision, recall, and F1
- ROC/AUC Curves: receiver operator characteristics and area under the curve
- Precision-Recall Curves: precision vs recall for different probability thresholds
- Confusion Matrices: visual description of class decision making
- Discrimination Threshold: find a threshold that best separates binary classes
Regression Visualization¶
- Prediction Error Plot: find model breakdowns along the domain of the target
- Residuals Plot: show the difference in residuals of training and test data
- Alpha Selection: show how the choice of alpha influences regularization
- Cook’s Distance: show the influence of instances on linear regression
Clustering Visualization¶
- K-Elbow Plot: select k using the elbow method and various metrics
- Silhouette Plot: select k by visualizing silhouette coefficient values
- Intercluster Distance Maps: show relative distance and size/importance of clusters
Model Selection Visualization¶
- Validation Curve: tune a model with respect to a single hyperparameter
- Learning Curve: show if a model might benefit from more data or less complexity
Target Visualization¶
- Balanced Binning Reference: generate a histogram with vertical lines showing the recommended value point to bin the data into evenly distributed bins
- Class Balance: see how the distribution of classes affects the model
- Feature Correlation: display the correlation between features and dependent variables
Text Visualization¶
- Term Frequency: visualize the frequency distribution of terms in the corpus
- t-SNE Corpus Visualization: use stochastic neighbor embedding to project documents
- Dispersion Plot: visualize how key terms are dispersed throughout a corpus
- UMAP Corpus Visualization: plot similar documents closer together to discover clusters
- PosTag Visualization: plot the counts of different parts-of-speech throughout a tagged corpus
… and more! Visualizers are being added all the time. Check the examples (or even the develop branch). Feel free to contribute your ideas for new Visualizers!
Getting Help¶
Can’t get someting to work? Here are places you can find help.
- The docs (you’re here!).
- Stack Overflow. If you ask a question, please tag it with “yellowbrick”.
- The Yellowbrick Google Groups Listserv.
- You can also Tweet or direct message us on Twitter @scikit_yb.
Open Source¶
The Yellowbrick license is an open source Apache 2.0 license. Yellowbrick enjoys a very active developer community; please consider Contributing!
Yellowbrick is hosted on GitHub. The issues and pull requests are tracked there.