Summary of Quantile Regression Using Random Forest Proximities, by Mingshu Li et al.
Quantile Regression using Random Forest Proximities
by Mingshu Li, Bhaskarjit Sarmah, Dhruv Desai, Joshua Rosaler, Snigdha Bhagat, Philip Sommer, Dhagash Mehta
First submitted to arxiv on: 5 Aug 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Machine Learning (cs.LG); Statistical Finance (q-fin.ST); Trading and Market Microstructure (q-fin.TR)
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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 introduces a novel approach to computing quantile regressions from random forests, which leverages the proximity learned by the model and infers the conditional distribution of the target variable. The authors propose a methodology that combines the strengths of quantile regression forests (QRF) with the benefits of using random forest proximities. They evaluate their method on publicly available datasets and demonstrate its superiority in approximating conditional target distributions and prediction intervals compared to traditional approaches to QRF. Furthermore, they show that their framework is more computationally efficient than existing methods. This paper has important implications for investors seeking to understand varying risk levels in financial markets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us better predict financial market movements by improving the way we analyze uncertainty. It’s like trying to guess where a stock price will be tomorrow, but instead of just guessing one number, we’re trying to figure out all the possible outcomes and how likely they are. The authors developed a new method that uses random forests to do this, which is better than other methods because it can handle lots of different scenarios at once and gives us more accurate predictions. |
Keywords
» Artificial intelligence » Random forest » Regression
