# Using Explainable Boosting Machine to Compare Idiographic and Nomothetic Approaches for Ecological Momentary Assessment Data

arXiv: https://arxiv.org/abs/2204.01689 · HTML: https://mlsanity.com/paper/2204.01689/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 19 references that are in the map.
- First posted on arXiv 4 Apr 2022.

## Builds on (most cited first, 2 of 2)

- [Distilling the Knowledge in a Neural Network](https://mlsanity.com/paper/1503.02531/) (2015)
- [InterpretML: A Unified Framework for Machine Learning Interpretability](https://mlsanity.com/paper/1909.09223/) (2019)

## Built on by (most cited first, 4 of 4)

- [Clustering individuals based on multivariate EMA time-series data](https://mlsanity.com/paper/2212.01159/) (2022)
- [Model-based Clustering of Individuals' Ecological Momentary Assessment Time-series Data for Improving Forecasting Performance](https://mlsanity.com/paper/2310.07491/) (2023)
- [Exploiting Individual Graph Structures to Enhance Ecological Momentary Assessment (EMA) Forecasting](https://mlsanity.com/paper/2403.19442/) (2024)
- [Explaining Clustering of Ecological Momentary Assessment Data Through Temporal and Feature Attention](https://mlsanity.com/paper/2405.04854/) (2024)

## Abstract (arXiv)

Previous research on EMA data of mental disorders was mainly focused on multivariate regression-based approaches modeling each individual separately. This paper goes a step further towards exploring the use of non-linear interpretable machine learning (ML) models in classification problems. ML models can enhance the ability to accurately predict the occurrence of different behaviors by recognizing complicated patterns between variables in data. To evaluate this, the performance of various ensembles of trees are compared to linear models using imbalanced synthetic and real-world datasets. After examining the distributions of AUC scores in all cases, non-linear models appear to be superior to baseline linear models. Moreover, apart from personalized approaches, group-level prediction models are also likely to offer an enhanced performance. According to this, two different nomothetic approaches to integrate data of more than one individuals are examined, one using directly all data during training and one based on knowledge distillation. Interestingly, it is observed that in one of the two real-world datasets, knowledge distillation method achieves improved AUC scores (mean relative change of +17% compared to personalized) showing how it can benefit EMA data classification and performance.