Summary of Conditional Density Estimation with Histogram Trees, by Lincen Yang et al.
Conditional Density Estimation with Histogram Trees
by Lincen Yang, Matthijs van Leeuwen
First submitted to arxiv on: 15 Oct 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 proposed Conditional Density Tree (CDTree) model is a non-parametric method for conditional density estimation that outperforms existing interpretable methods in terms of accuracy and robustness. By using a decision tree with histogram-based leaf nodes, the CDTree provides a richer understanding of the data than traditional regression models. The authors formalize the problem of learning a CDTree using the minimum description length principle, which eliminates the need for hyperparameter tuning. An iterative algorithm is proposed to find the optimal histogram for each node split. Experimental results show that CDTrees achieve better log-loss scores and are more robust against irrelevant features compared to existing tree-based methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The CDTree is a new way to understand data by looking at how likely something is to happen based on other factors. It’s like asking “what’s the chance of this happening if this other thing happens?” Right now, there aren’t many ways to do this that are easy to understand. The CDTree helps with this by using a simple and visual way to model data, which makes it easier to see what’s going on. This is important because some problems need us to understand the whole picture, not just part of it. |
Keywords
» Artificial intelligence » Decision tree » Density estimation » Hyperparameter » Regression
