Roadmap#
Purpose of this document#
This document list general directions that core contributors are interested to see developed in scikit-plots. The fact that an item is listed here is in no way a promise that it will happen, as resources are limited. Rather, it is an indication that help is welcomed on this topic.
Statement of purpose: Scikit-plots in 2024#
Since 2017 after the inception of Scikit-plot now Scikit-plots, much has changed in the world of machine learning.
Architectural / general goals#
The list is numbered not as an indication of the order of priority, but to make referring to specific points easier. Please add new entries only at the bottom. Note that the crossed out entries are already done, and we try to keep the document up to date as we work on these issues.
Improved handling of Pandas DataFrames
document current handling
Improved handling of categorical features
Handling mixtures of categorical and continuous variables
More didactic documentation
More and more options have been added to scikit-plots. As a result, the documentation is crowded which makes it hard for beginners to get the big picture. Some work could be done in prioritizing the information.
Passing around information that is not (X, y): Feature properties
Per-feature handling (e.g. “is this a nominal / ordinal / English language text?”)
Passing around information that is not (X, y): Target information
We have no way to handle a mixture of categorical and continuous targets.
Make it easier for external users to write Scikit-plots-compatible components
More self-sufficient running of scikit-plots-contrib or a similar resource