Extract interactions from complex model using SHAP and add to linear model..
Project description
Welcome to InteractionTransformer
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:
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
- Github: @jlevy44
Project details
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
Hashes for interactiontransformer-0.1.1.tar.gz
Algorithm | Hash digest | |
---|---|---|
SHA256 | 18cf1bee229c5c165b7f95cb0afcd286e11732a7830e4bfc058a10cd1b1cd589 |
|
MD5 | f9db6c762bf7c8e70132c303e68cf252 |
|
BLAKE2b-256 | b69717c41b113b3bd2d462a21f90692f8cc73b1d8750ee9c80aefe3784b6b9d6 |