Combining Prediction and Interpretation in Decision Trees (PrInDT) -- a Linguistic Example
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.
Code (0)
등록된 구현이 없습니다.
Tasks
PredictionSimilar Papers 제목 키워드 기반
Kernel Density Decision Trees
We propose kernel density decision trees (KDDTs), a novel fuzzy decision tree (FDT) formalism based on kernel density estimation that achieves state-of-the-art prediction performance often matching or exceeding that of c…
Density EstimationFeature Learning for Interpretable, Performant Decision Trees
Decision trees are regarded for high interpretability arising from their hierarchical partitioning structure built on simple decision rules. However, in practice, this is not realized because axis-aligned partitioning of…
Cluster-Based Random Forest Visualization and Interpretation
Random forests are a machine learning method used to automatically classify datasets and consist of a multitude of decision trees. While these random forests often have higher performance and generalize better than a sin…
Splitting the Difference: Interpretable Causal Forests for Treatment Effect Heterogeneity and Bias
In various fields, such as medicine and marketing, accurately predicting individual treatment effects holds significant promise. However, achieving reliable predictions alone is often insufficient for making informed dec…
ProGReST: Prototypical Graph Regression Soft Trees for Molecular Property Prediction
In this work, we propose the novel Prototypical Graph Regression Self-explainable Trees (ProGReST) model, which combines prototype learning, soft decision trees, and Graph Neural Networks. In contrast to other works, our…
Graph RegressionMolecular Property PredictionPredictionProperty Prediction+1