Summary of Randomization Can Reduce Both Bias and Variance: a Case Study in Random Forests, by Brian Liu and Rahul Mazumder
Randomization Can Reduce Both Bias and Variance: A Case Study in Random Forests
by Brian Liu, Rahul Mazumder
First submitted to arxiv on: 20 Feb 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 Random forests appear to reduce bias compared to bagging, a phenomenon first noted by Breiman (2001). Motivated by Mentch’s 2020 paper on randomization, we explore how random forests uncover patterns missed by bagging. Our empirical results show that in the presence of such patterns, random forests reduce both bias and variance, outperforming bagging ensembles when signal-to-noise ratio is high. This insight into real-world success and differences between random forests and bagging enhances our understanding of the importance of tuning mtry. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Random forests are a type of machine learning model that can be used for tasks like predicting what someone will buy based on their past purchases. Sometimes, these models seem to work better than other types of models, even when there isn’t much information about what’s going to happen next. Researchers have wondered why this is the case and found that random forests are able to find patterns in the data that other models miss. This means that they can be very good at predicting things when there is a lot of useful information available. The researchers who wrote this paper want to understand more about how random forests work and what makes them so good. |
Keywords
* Artificial intelligence * Bagging * Machine learning
