Random Forest: A Classification and Regression Tool for Compound Classification and QSAR Modeling

By V. Svetnik, Andy Liaw, Christopher Tong, J. Culberson, Robert P. Sheridan, B. Feuston. 2003

3,318 citations recorded by the source corpus.

Subfields: Computational Theory and Mathematics, Molecular Biology, Computational Chemistry & Drug Discovery

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Citations in the corpus

Forest Floor Visualizations of Random Forests (2016, background): “For RF modeling, hyper parameter tuning is not critical and default parameters will yield acceptable model fits and visualizations in most situations [10, 23].”
Local Linear Forests (2018, methodology): “Biau (2012) used a different type of sample splitting, wherein for each tree the data is split into two sets (Dn and D′ n).”
The Fact Extraction and VERification (FEVER) Shared Task (2018, background): “…Enhanced LSTM (Chen et al., 2016), Decomposable Attention (Parikh et al., 2016), Transformer Model (Radford and Salimans, 2018), Random Forests (Svetnik et al., 2003) and ensembles thereof), these are not specific to the task and it is difficult to assess their impact due to the differences in…”
Consistency of Random Forests (2014, neutral): “…(winning code of the EMC data science global hackathon in 2012, see http://www.kaggle.com/c/dsg-hackathon ), chemoinformatics (Svet-nik et al., 2003), ecology (Prasad et al., 2006; Cutler et al., 2007), 3D object recognition (Shotton et al., 2011), and bioinformatics (D´ıaz-Uriarte and de…”
Adaptive Concentration of Regression Trees, with Application to Random Forests (2015, background): “[42] Vladimir Svetnik, Andy Liaw, Christopher Tong, J Christopher Culberson, Robert P Sheridan, and Bradley P Feuston.”
MiPred: classification of real and pseudo microRNA precursors using random forest prediction model with combined features (2007, methodology): “Because OOB sequences have not been used in the tree construction, one can use them to estimate the prediction performance ( 28 ).”
A machine learning approach to predicting protein-ligand binding affinity with applications to molecular docking (2010, positive): “Second, instead of using all features, RF selects the best split at each node of the tree from a typically small number (mtry) of randomly chosen features.”
Big Data Creates New Opportunities for Materials Research: A Review on Methods and Applications of Machine Learning for Materials Design (2019, positive): “The decision tree [45] and random forest [46] algorithms are the two most commonly used classification algorithms.”