Combining Prediction and Interpretation in Decision Trees (PrInDT) -- a Linguistic Example

TL;DR

This paper introduces PrInDT, a new method that combines prediction and interpretation in decision trees, specifically applied to modeling linguistic variation with improved suitability.

Contribution

The paper presents PrInDT, a novel statistical approach that enhances decision trees by integrating prediction and interpretability for linguistic data analysis.

Findings

PrInDT improves modeling of linguistic variation.

Combining prediction and interpretation enhances decision tree effectiveness.

PrInDT is suitable for linguistic applications.

Abstract

In this paper, we show that conditional inference trees and ensembles are suitable methods for modeling linguistic variation. As against earlier linguistic applications, however, we claim that their suitability is strongly increased if we combine prediction and interpretation. To that end, we have developed a statistical method, PrInDT (Prediction and Interpretation with Decision Trees), which we introduce and discuss in the present paper.