Summary of Fast Estimation Of Partial Dependence Functions Using Trees, by Jinyang Liu et al.
Fast Estimation of Partial Dependence Functions using Trees
by Jinyang Liu, Tessa Steensgaard, Marvin N. Wright, Niklas Pfister, Munir Hiabu
First submitted to arxiv on: 17 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Machine Learning (stat.ML)
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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 This paper proposes a novel tree-based estimator called FastPD, which efficiently estimates arbitrary Partial Dependence (PD) functions. Unlike existing methods, such as Shapley additive explanations (SHAP), FastPD consistently estimates the desired population quantity, even when features are correlated. The proposed method improves the complexity of existing estimators from quadratic to linear in the number of observations for moderately deep trees. FastPD can be used to extract various PD-based interpretations, including SHAP values, PD plots, and higher-order interaction effects. This paper builds upon recent work connecting PD-based interpretation methods and argues that SHAP values can be misleading due to merging main and interaction effects into a single local effect. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research paper introduces a new way to understand how machine learning models make predictions. The current methods for doing this, called Partial Dependence (PD) functions, have some limitations. A group of researchers created a new algorithm, called FastPD, that can efficiently estimate these PD functions. This means it can provide more accurate information about how the model is making predictions. The new method can also help identify different types of effects, such as main and interaction effects. This could be useful in many areas, including science, healthcare, and finance. |
Keywords
» Artificial intelligence » Machine learning
