Summary of A Tree-based Varying Coefficient Model, by Henning Zakrisson and Mathias Lindholm
A tree-based varying coefficient model
by Henning Zakrisson, Mathias Lindholm
First submitted to arxiv on: 11 Jan 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Machine Learning (cs.LG)
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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 proposed tree-based varying coefficient model uses a cyclic gradient boosting machine to model coefficient functions, allowing for dimension-wise early stopping and feature importance scores. This approach reduces the risk of overfitting while revealing differences in model complexity across dimensions. The model is evaluated on simulated and real data examples, showing comparable out-of-sample loss results to neural network-based VCMs like LocalGLMnet. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new way to study patterns in data uses a special kind of machine learning called tree-based varying coefficient models. This method helps us understand how different parts of the data are related by using something called cyclic gradient boosting machines. This allows us to see which parts of the data are important and which ones aren’t, making it easier to understand the results. |
Keywords
* Artificial intelligence * Boosting * Early stopping * Machine learning * Neural network * Overfitting
