Yellowbrick is an open source, pure Python project that extends the scikit-learn API with visual analysis and diagnostic tools. The Yellowbrick API also wraps matplotlib to create publication-ready figures and interactive data explorations while still allowing developers fine-grain control of figures. For users, Yellowbrick can help evaluate the performance, stability, and predictive value of machine learning models and assist in diagnosing problems throughout the machine learning workflow. This version primarily repairs the dependency issues we faced with scipy 1.6, scikit-learn 0.24 and Python 3.6 (or earlier). As part of the rapidly changing Python library landscape, we’ve been forced to react quickly to dependency changes, even where those libraries have been responsibly issuing future and deprecation warnings. Major Changes: Added WordCorrelationPlot Visualizer Built tests for using sklearn pipeline with visualizers Allowed Marker Style to be specified in Validation Curve Visualizer Fixed get_params for estimator wrapper to prevent AttributeError Updated missing values visualizer to handle multiple data types and work on both numpy arrays and pandas data frames. Added pairwise distance metrics to scoring metrics in KElbowVisualizer Minor Changes: Pegged Numba to v0.55.2 Updated Umap to v0.5.3 Fixed Missing labels in classification report visualizer Updated Numpy to v1.22.0 Documentation: The Spanish language Yellowbrick docs are now live: https://www.scikit-yb.org/es/latest/ Added Dropping curve documentation Added new example Notebook for Regression Visualizers Fixed Typo in PR section of getting started docs Fixed Typo in rank docs Updated docstring in kneed.py utility file Clarified how to run ‘make html’ in PR template Infrastructure/Housekeeping/documentation Added ability to run linting Actions on PRs Implemented black code formatting as pre-commit hook