Combining Prediction and Interpretation in Decision Trees (PrInDT) -- a Linguistic Example
This is an incremental improvement for linguistics researchers, focusing on enhancing decision tree methods for linguistic data analysis.
The paper tackles the problem of modeling linguistic variation by developing PrInDT, a method that combines prediction and interpretation in decision trees, claiming it significantly increases suitability compared to earlier applications.
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.