ArticleFrontiers in artificial intelligence2020
Interpretability With Accurate Small Models.
Article in Frontiers in artificial intelligence, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
3 citing papers in PubMed.
- A Biotechnological Platform Based on the Newly Isolated Streptococcus thermophilus D4 to Obtain Viable Biomass and Exopolysaccharides for Enterocytes Wound Healing.Biotechnology and bioengineering · 2025Article
- Post-Analysis of Predictive Modeling with an Epidemiological Example.Healthcare (Basel, Switzerland) · 2021Article
- Evolutionary Algorithm for Improving Decision Tree with Global Discretization in Manufacturing.Sensors (Basel, Switzerland) · 2021Article
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Models often need to be constrained to a certain size for them to be considered interpretable. For example, a decision tree of depth 5 is much easier to understand than one of depth 50. Limiting model size, however, often reduces accuracy. We suggest a practical technique that minimizes this trade-off between interpretability and classification accuracy. This enables an arbitrary learning algorithm to produce highly accurate small-sized models. Our technique identifies the training data distribution to learn from that leads to the highest accuracy for a model of a given size. We represent the training distribution as a combination of sampling schemes. Each scheme is defined by a parameterized probability mass function applied to the segmentation produced by a decision tree. An Infinite Mixture Model with Beta components is used to represent a combination of such schemes. The mixture model parameters are learned using Bayesian Optimization. Under simplistic assumptions, we would need to optimize for
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Registered trials
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