Explainable Boosted Linear Regression

This is the implementation of explainable boosted linear regression. It is a new boosting technique that relies on the residuals of weak learning decision trees to learn complex non-linear features while ensuring explainability in the model.

Setup

In order to run the following, you will need to have r packages installed, since EBLR relies on r's implementation fo decision trees. This was chosen since r-forecasts decision tree has important pruning that is needed inside EBLR.

The package can be downloaded by coming into the directory and installing eblr locally.

$ pip install .

Ensure that you have R set up on your computer as well, since EBLR uses r's forecast. Ensure that r has been correctly installed and linked to rpy by running:

$ python -m rpy2.situation

You will then need to install some r-packages. This can be done by opening an R-shell and running:

> install.packages("rpart")
> install.packages("treeClust")

Example

A sample dataset has been included to demonstrate how EBLR works. Navigate to the examples/ directory in the repository to run the notebook.