Interpretable machine learning models for predicting with missing values
Licentiate thesis, 2023
In this thesis, we focus on predictions in the presence of incomplete data at test time, using interpretable models that allow humans to understand the predictions. Interpretability is especially necessary when important decisions are at stake, such as in healthcare.
First, we investigate, the situation where variables are missing in recurrent patterns and sample sizes are small per pattern. We propose SPSM that allows coefficient sharing between a main model and pattern submodels in order to make efficient use of data and to be independent on imputation. To enable interpretability, the model can be expressed as a short description introduced by sparsity.
Then, we explore situations where missingness does not occur in patterns and suggest the sparse linear rule model MINTY that naturally trades off between interpretability and the goodness of fit while being sensitive to missing values at test time. To this end, we learn replacement variables, indicating which features in a rule can be alternatively used when the original feature was not measured, assuming some redundancy in the covariates.
Our results have shown that the proposed interpretable models can be used for prediction with missing values, without depending on imputation. We conclude that more work can be done in evaluating interpretable machine learning models in the context of missing values at test time.
missing values
Machine learning
healthcare
interpretable machine learning
Author
Lena Stempfle
Chalmers, Computer Science and Engineering (Chalmers), Data Science and AI
Lena Stempfle, Fredrik D. Johansson - Learning replacement variables in interpretable rule-based models
Sharing Pattern Submodels for Prediction with Missing Values
Proceedings of the AAAI Conference on Artificial Intelligence,;Vol. 37(2023)p. 9882-9890
Paper in proceeding
Predicting progression and cognitive decline in amyloid-positive patients with Alzheimer’s disease
Alzheimers Research and Therapy,;Vol. 13(2021)
Journal article
WASP AI/MLX Professorship
Wallenberg AI, Autonomous Systems and Software Program, 2019-08-01 -- 2023-08-01.
Subject Categories
Computer and Information Science
Infrastructure
C3SE (Chalmers Centre for Computational Science and Engineering)
Publisher
Chalmers
Analysen, EDIT Building, Hörsalsvägen 11, Chalmers
Opponent: Gaël Varoquaux, Ph.D, Research Director, INRIA (French Computer Science National research), France