Marginal Contribution Feature Importance
Measuring Feature Importance of data to allow explainability. This tool uses the MCI approach to measure feature importance for the global setting when a scientists are trying to understand the contribution of different features in the data to a given outcome.
In many applications of machine learning records of data contains sets of items. For example, medical records may contain sets of medications and astrological data may contain sets of stars. Set-Tree is a python tool that allows applying decision trees and boosted decision trees to such data.