Randomness control and reproducibility study of random forest algorithm in R and Python
When it comes to the safety of cosmetic products, compliance with regulatory standards is crucialto guarantee consumer protection against the risks of skin irritation. Toxicologists must thereforebe fully conversant with all risks. This applies not only to their day-to-day work, but also to allthe algorithms they integrate into their routines. Recognizing this, ensuring the reproducibility ofalgorithms becomes one of the most crucial aspects to address.However, how can we prove the robustness of an algorithm such as the random forest, that reliesheavily on randomness? In this report, we will discuss the strategy of integrating random forest intoocular tolerance assessment for toxicologists.We will compare four packages: randomForest and Ranger (R packages), adapted in Python via theSKRanger package, and the widely used Scikit-Learn with the RandomForestClassifier() function.Our goal is to investigate the parameters and sources of randomness affecting the outcomes ofRandom Forest algorithms.By setting comparable parameters and using the same Pseudo-Random Number Generator (PRNG),we expect to reproduce results consistently across the various available implementations of therandom forest algorithm. Nevertheless, this exploration will unveil hidden layers of randomness andguide our understanding of the critical parameters necessary to ensure reproducibility across all fourimplementations of the random forest algorithm.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Quantifying Inherent Randomness in Machine Learning Algorithms
Most machine learning (ML) algorithms have several stochastic elements, and their performances are affected by these sources of randomness. This paper uses an empirical study to systematically examine the effects of two …
BIG-bench Machine LearningEvaluating the method reproducibility of deep learning models in the biodiversity domain
Artificial Intelligence (AI) is revolutionizing biodiversity research by enabling advanced data analysis, species identification, and habitats monitoring, thereby enhancing conservation efforts. Ensuring reproducibility …
Deep LearningSynthesizing Irreproducibility in Deep Networks
The success and superior performance of deep networks is spreading their popularity and use to an increasing number of applications. Very recent works, however, demonstrate that modern day deep networks suffer from irrep…
Exogenous Randomness Empowering Random Forests
We offer theoretical and empirical insights into the impact of exogenous randomness on the effectiveness of random forests with tree-building rules independent of training data. We formally introduce the concept of exoge…
Towards Training Reproducible Deep Learning Models
Reproducibility is an increasing concern in Artificial Intelligence (AI), particularly in the area of Deep Learning (DL). Being able to reproduce DL models is crucial for AI-based systems, as it is closely tied to variou…
Deep LearningGPU