Work

Building Trees for Probabilistic Prediction via Scoring Rules

Year: 2024

Type: article

Abstract: Decision trees built with data remain in widespread use for nonparametric prediction. Predicting probability distributions is preferred over point predictions when uncertainty plays a prominent role in analysis and decision-making. We study modifying a tree to produce nonparametric predictive distributions. We find the standard method for building trees may not result in good predictive distributions and propose changing the splitting criteria for trees to one based on proper scoring rules. Analysis of both simulated data and several real datasets demonstrates that using these new splitting criteria results in trees with improved predictive properties considering the entire predictive distribution.

Source: Technometrics

DOI: 10.1080/00401706.2024.2343062

Authors: Sara Shashaani, Özge Sürer, Matthew Plumlee, Seth Guikema

Institutions: North Carolina State University, Miami University, Northwestern University, University of Michigan

Language: English

Topic: Data Mining Algorithms and Applications

Subfield: Information Systems

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

Funders: National Science Foundation, National Stroke Foundation

Awards: G1927392530, Collaborative Research: Calibrating Digital Twins in the Era of Big Data with Stochastic Optimization