Summary of Function Trees: Transparent Machine Learning, by Jerome H. Friedman
Function Trees: Transparent Machine Learning
by Jerome H. Friedman
First submitted to arxiv on: 19 Mar 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 method represents a general multivariate function as a tree of simpler functions, uncovering the global internal structure of the function by identifying joint influences of input subsets. This allows for rapid computation and visualization of main and interaction effects up to high order, including graphical representation of effects involving up to four variables. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A machine learning algorithm’s output can be represented as a multivariate function of its inputs. Understanding this function’s global properties helps explain the data generation process and model predictions. The method creates a “function tree” that reveals the combined influences of input subsets, enabling fast computation and visualization of main and interaction effects up to high order. |
Keywords
* Artificial intelligence * Machine learning
