Integrating scientific knowledge into machine learning using interactive decision trees

Abstract

Decision Trees (DT) describe a type of 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, such as significant biases, leading to potentially physically unrealistic results. We develop interactive DT (iDT) that put humans in the loop to 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 interactively create new composite variables, change the variable and threshold to split, prune and group variables based on their physical meaning. We demonstrate with three case studies how iDT overcomes problems with current DT thus achieving higher interpretability and robustness of the result.