Four interpretable algorithms that you should use in 2022
Dernière mise à jour : 13 mai
V. Margot. Towards Data Science. January, 4th 2022.
Abstract: The new year has begun, and it is the time for good resolutions. One of them could be to make decision-making processes more interpretable. To help you do this, I present four interpretable rule-based algorithms. These four algorithms share the use of ensemble of decision trees as rule generator (like Random Forest, AdaBoost, Gradient Boosting, etc.). In other words, each of these interpretable algorithms starts its process by fitting a black box model and generating an interpretable rule ensemble model. Even though they are all claimed to be interpretable, they were developed with a different idea of interpretability. As you may know, this concept is difficult to be mathematically well posed. Therefore, authors designed interpretable algorithms with their own definition of interpretability.