Periodic split method

This paper presents specific features of the Craft AI Machine Learning engine that enable it to better take into account typical rhythms in human activities. In particular, it improves the quality and explainability of predictive models related to those.

12/07/2017

R&D

Tous les articles

Sommaire

This work was presented at APIA 2017 in Caen (France) and published in its proceedings.

Abstract

periodic split

Placing your trust in algorithms is a major issue in society today. This article introduces a novel split method for decision tree generation algorithms aimed at improving the quality/readability ratio of generated decision trees. We focus on human activities learning that allow the definition of new temporal features. By virtue of these features, we present here the periodic split method, which produces similar or superior quality trees with reduced tree depth.

Une plateforme compatible avec tout l’écosystème

aws
Azure
Google Cloud
OVH Cloud
scikit-lean
PyTorch
Tensor Flow
XGBoost
jupyter
PC
Python
R
Rust
mongo DB