It was more akin to a very simple formula or decision tree designed by a human committee. This disconnect highlights a growing issue. ... The complexity of the algorithm itself may also vary.

After training decision trees against data, the algorithm is then run against new data in a test set. Before algorithm training, a test set is randomly extracted from the original set.

The study of decision trees and optimisation techniques remains at the forefront of modern data science and machine learning. Decision trees, with their inherent interpretability and efficiency ...

At the heart of this work is the concern that algorithms are often opaque, biased, ... decision trees — that are adding real value to the bottom line of many organizations.