Summary of Randomness Control and Reproducibility Study Of Random Forest Algorithm in R and Python, by Louisa Camadini et al.
Randomness control and reproducibility study of random forest algorithm in R and Python
by Louisa Camadini, Yanis Bouzid, Maeva Merlet, Léopold Carron
First submitted to arxiv on: 22 Aug 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- 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 This paper tackles a crucial challenge in ensuring the safety of cosmetic products by investigating the reproducibility of algorithms, specifically the Random Forest method. To guarantee consumer protection against skin irritation risks, toxicologists must be aware of all potential risks and ensure that their daily work and integrated algorithms meet regulatory standards. The researchers integrate the Random Forest algorithm into ocula tolerance assessment, comparing four packages: randomForest, Ranger, SKRanger, and Scikit-Learn’s RandomForestClassifier(). They aim to identify parameters and sources of randomness affecting the outcomes and reproduce results consistently across implementations. This study will uncover hidden layers of randomness and provide a deeper understanding of the critical parameters necessary for reproducibility. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In this study, scientists are working to make sure that cosmetic products are safe for use on skin. They want to understand how an algorithm called Random Forest works so they can be confident it’s reliable. The researchers test four different ways to use this algorithm and compare the results. This helps them figure out what makes the algorithm work and how they can get the same answers every time. |
Keywords
» Artificial intelligence » Random forest
