EDA Toolkit 0.0.8a

Version 0.0.8 Release Notes

We are excited to announce the release of version 0.0.8, which introduces significant enhancements and new features to improve the usability and functionality of our toolkit.

New Features:

1. Optional file_prefix in stacked_crosstab_plot Function

   • The stacked_crosstab_plot function has been updated to make the file_prefix argument optional. If the user does not provide a file_prefix, the function will now automatically generate a default prefix based on the col and func_col parameters. This change streamlines the process of generating plots by reducing the number of required arguments.
   • Key Improvement:
     • Users can now omit the file_prefix argument, and the function will still produce appropriately named plot files, enhancing ease of use.
     • Backward compatibility is maintained, allowing users who prefer to specify a custom file_prefix to continue doing so without any issues.

2. Introduction of 3D and 2D Partial Dependence Plot Functions

   • Two new functions, plot_3d_pdp and plot_2d_pdp, have been added to the toolkit, expanding the visualization capabilities for machine learning models.
     • plot_3d_pdp: Generates 3D partial dependence plots for two features, supporting both static visualizations (using Matplotlib) and interactive plots (using Plotly). The function offers extensive customization options, including labels, color maps, and saving formats.
     • plot_2d_pdp: Creates 2D partial dependence plots for specified features with flexible layout options (grid or individual plots) and customization of figure size, font size, and saving formats.
   • Key Features:
     • Compatibility: Both functions are compatible with various versions of scikit-learn, ensuring broad usability.
     • Customization: Extensive options for customizing visual elements, including figure size, font size, and color maps.
     • Interactive 3D Plots: The plot_3d_pdp function supports interactive visualizations, providing an enhanced user experience for exploring model predictions in 3D space.

Impact:

• These updates improve the user experience by reducing the complexity of function calls and introducing powerful new tools for model interpretation.
• The optional file_prefix enhancement simplifies plot generation while maintaining the flexibility to define custom filenames.
• The new partial dependence plot functions offer robust visualization options, making it easier to analyze and interpret the influence of specific features in machine learning models.

We encourage users to explore these new features and provide feedback on their experience. As always, we remain committed to continuous improvement and welcome suggestions for future updates.