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RStudio r-software randomforest
R Software Randomforest, supplied by RStudio, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/product/randomforests+software/randomforest/pmc10125289-136-14-9
Average 90 stars, based on 1 article reviews
r-software randomforest - by Bioz Stars, 2026-10
90/100 stars

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Article Title: Comparison of machine learning algorithms and multiple linear regression for live weight estimation of Akkaraman lambs
Article Snippet: Eight machine learning algorithms were utilized in this study, including Artificial Neural Network (ANN) using various packages such as “randomForest” for RF, “e1071” for SVM and SVR, “xgboost” for XGBoost and GBoost, “brnn” for BRNN, “RSNNS” for RBRNN, “rpart” for CART, and “party” for Exhaustive CHAID, CHAID, and “earth” for MARS algorithms, all within the RStudio environment (R Studio Team ).

Article Title: Data-driven message optimization in dynamic sports media: an artificial intelligence approach to predict consumer response
Article Snippet: Five supervised learning models were trained and tested in RStudio: Gradient Boost (gbm; Greenwell et al. (2022)), XGBoost (xgboost; Chen and He (2023)), SVM (e1071; Meyer et al. (2019)), and Random Forest (twice; randomForest; Liaw and Wiener (2002)).

Article Title: Contribution of mycorrhizal symbiosis and root strategy to red clover aboveground biomass under nitrogen addition and phosphorus distribution.
Article Snippet: To assess the importance of total root length at different diameters on aboveground biomass under various treatments, the “randomForest” package in R (adonis in vegan package, RStudio) was utilized (Oksanen et al. 2017; R Development Core Team 2012).

Article Title: Predicting the site productivity of forest tree species using climate niche models
Article Snippet: All models were processed in R interfacing with RStudio 4.1.3 (R Core Team, 2013), with package ‘randomForest’ for RF (Cutler et al., 2018), package ‘dismo’ for Maxent (Hijmans et al., 2021), package ‘gbm’ for GBM (Ridgeway, 2020), package ‘mgcv’ for GAM (Wood, 2021), respectively.

Article Title: A microRNA-based dynamic risk score for type 1 diabetes.
Article Snippet: Classification models were built on the training datasets of four contexts together using the random forest workflow, using the packages randomForest (v.4.7-1.1) and Caret (v.6.0-94)72 in R (v.4.2.2)73 and RStudio (v.2023.12.1-402)74.

Article Title: GABP Promotes Mesangial Cell Proliferation and Renal Fibrosis Through GLI1 in Diabetic Nephropathy
Article Snippet: Patients in DN group and DM group were used to establish a binary machine learning prediction model. Randomforest (R package, v4.7‐1.1) and caret (R package, v6.0‐94) are used to establish the machine learning model in R 4.2.3 and RStudio.

Article Title: Agent-based modeling of neuronal mitochondrial dynamics using intrinsic variables of individual mitochondria.
Article Snippet: ArticleiScience Agent-based modeling of neuronal mitochondrial dynamics using intrinsic variables of individual mitochondria

Article Title: Aryl Organophosphate Esters and Hemostatic Disruption: Identifying Risk through Machine Learning and Experimental Validation.
Article Snippet: Organophosphate esters (OPEs) have emerged as a significant environmental concern due to their widespread occurrence and potential human health risks.. The presence of OPEs in human blood suggests direct interactions with hematological components, which may compromise hemostatic balance and lead to adverse health outcomes.. Despite the critical role of hemostatic balance in maintaining blood stability, the effects of OPEs on this system remain poorly understood.



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