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Table 3 Overview of the different machine learning methods and parameter settings used in the study

From: Evaluating parameters for ligand-based modeling with random forest on sparse data sets

FEST and Scikit RF Scikit SVM
Max features\(^{\hbox {a}}\) Trees C \(\gamma\)
0.1 10 0.01 \(1\times 10^{-6}\)
0.3 30 0.1 \(3\times 10^{-6}\)
1.0 100 1 \(1\times 10^{-5}\)
3.0 300 10 \(3\times 10^{-5}\)
10.0 1000 100 \(1\times 10^{-4}\)
   1000 \(3\times 10^{-4}\)
   \(1\times 10^{4}\) 0.001
   \(1\times 10^{5}\) 0.003
   \(1\times 10^{6}\) 0.01
   \(1\times 10^{7}\) 0.03
   \(1\times 10^{8}\) 0.1
  1. \(^{\hbox {a}}\)Values indicate the multiplicating factor by the square root of the number of features