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Extract interactions from complex model using SHAP and add to linear model..

Project description

Welcome to InteractionTransformer

Version

Extract meaningful interactions from machine learning models to obtain machine-learning performance with statistical model interpretability.

Code accompanying the manuscript: "Don't Dismiss Logistic Regression: The Case for Sensible Extraction of Interactions in the Era of Machine Learning"
Preprint: https://www.biorxiv.org/content/10.1101/2019.12.15.877134v1

Please see our wiki for more information on setting up and running this package: https://github.com/jlevy44/InteractionTransformer/wiki

QUICKSTART DEMOS can be found here:

Python: https://github.com/jlevy44/InteractionTransformer/blob/master/demos/InteractionTransformerPythonDemo.ipynb

R: https://github.com/jlevy44/InteractionTransformer/blob/master/demos/InteractionTransformerRDemo.Rmd

Install

Python: We recommend installing using anaconda (https://www.anaconda.com/distribution/). First, install anaconda. Then, run:

conda create -n interaction_transform_environ python=3.7
conda activate interaction_transform_environ

Finally:

pip install interactiontransformer

R
First, install the python pip package. Then:

devtools::install_github("jlevy44/interactiontransformer")

Or:

library(devtools)
install_github("jlevy44/interactiontransformer")

Alternative Python Install Instructions

git clone https://github.com/jlevy44/InteractionTransformer
cd InteractionTransformer
pip install . # make sure conda is running

Author

👤 Joshua Levy

Project details


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