Its scope is limited only for Ordinary Least Squares, setting the building block for incorporating the Generalized Linear Models provided in the scikit-learn module.
Insights
[ ] Thoroughly read the documentation on django-ai's API - specially on "Design Goals", "Engine related" and "Supervised Learning Technique"
[ ] Create a class on the appropriate place that inherits from SupervisedLearningTechique and implements Regression.
[ ] Implement on that class all the methods in the django-ai's API accordingly for incorporating this technique implementation - a reference implementation can be found here, which is the analogous for SVM using the same engine.
Note: The class and all fields should be documented in a way that Sphinx can generate the documentation
[ ] Create a simple test which proves the correct functioning of the implementation and covers all the essential parts of the implementation, analogous to this one.
[ ] Create the admin front-end for the technique, like is done here.
[ ] Create the documentation for the technique, like is done here
[ ] Provide an example of the technique like is done here.
Note that the example is not about a Linear Regression, but how to incorporate the use of a Linear Regression into a Django application to improve its functionality or solve an issue seamlessly.
[ ] You may have to create tests accordingly so the general testing coverage does not descend, see here.
The goal of this issue is to add support for Linear Regression using the scikit-learn engine for django-ai.
Its scope is limited only for Ordinary Least Squares, setting the building block for incorporating the Generalized Linear Models provided in the scikit-learn module.
Insights