Integrating Scientific Knowledge into Machine Learning using Interactive Decision Trees

This is a Preprint and has not been peer reviewed. This is version 2 of this Preprint.

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Authors

Georgios Sarailidis , Thorsten Wagener , Francesca Pianosi 

Abstract

Decision Trees (DT) is a machine learning method that has been widely used in the geosciences to automatically extract patterns from complex and high dimensional data. However, like any data-based method, the application of DT is hindered by data limitations and potentially physically unrealistic results. We develop interactive DT (iDT) that put the human in the loop and integrate the power of experts’ scientific knowledge with the power of the algorithms to automatically learn patterns from large datasets. We created an open-source Python toolbox that implements the iDT framework. Users can create new composite variables, manually change the variable and threshold to split, manually prune and group variables based on their physical meaning. We demonstrate with three case studies that iDT help experts incorporate their knowledge in the DT development achieving higher interpretability.

DOI

https://doi.org/10.31223/X5PP75

Subjects

Civil and Environmental Engineering, Engineering, Environmental Engineering

Keywords

Interactive Decision Trees, Human-in-the-Loop, Interpretability, Open-source toolbox

Dates

Published: 2021-07-24 07:34

Last Updated: 2022-03-29 14:30

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License

CC BY Attribution 4.0 International

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