Yellowbrick v1.3

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:

• Implement new set_params and get_params on ModelVisualizers to ensure wrapped estimator is being correctly accessed via the new Estimator methods.

• Freeze the test dependencies to prevent variability in CI (must periodically review dependencies to ensure we’re testing what our users are experiencing).

• Change model param to estimator param to ensure that Visualizer arguments match their property names so that inspect works with get and set params and other scikit-learn utility functions.

Minor Changes:

• Import scikit-learn private API _safe_indexing without error.

• Remove any calls to set_params in Visualizer __init__ methods.

• Modify test fixtures and baseline images to accommodate new sklearn implementation

• Set the numpy dependency to be less than 1.20 because this is causing Pickle issues with joblib and umap

• Add shuffle=True argument to any CV class that uses a random seed.

• Set our CI matrix to Python and Miniconda 3.7 and 3.8

• Correction in README regarding ModelVisualizer API.