Summary of Interpretable Generalized Additive Models For Datasets with Missing Values, by Hayden Mctavish et al.
Interpretable Generalized Additive Models for Datasets with Missing Values
by Hayden McTavish, Jon Donnelly, Margo Seltzer, Cynthia Rudin
First submitted to arxiv on: 3 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper proposes a novel machine learning approach, M-GAM (Missing-Value Generalized Additive Modeling), designed to tackle datasets with missing feature values. This challenge arises when applying traditional models that rely on complete data. To address this issue, M-GAM incorporates missingness indicators and their interactions while maintaining sparsity through l0 regularization. The authors demonstrate that M-GAM achieves similar or better accuracy compared to existing methods while significantly improving model sparsity. This is particularly important for tasks where interpretability and feature importance are crucial. The paper’s findings have implications for various applications, including data imputation, feature selection, and predictive modeling. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine trying to build a machine learning model using incomplete data. Some pieces of information might be missing, making it hard to get accurate results. A team of researchers has created a new way to handle this problem called M-GAM. It’s like adding special “missing” flags that help the model understand what’s missing and make better predictions. This approach is more efficient than other methods and can help us build better models for tasks like image recognition, speech recognition, and forecasting weather. |
Keywords
» Artificial intelligence » Feature selection » Machine learning » Regularization
