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 | \(1\times 10^{-4}\) |
| Â | Â | 1000 | \(3\times 10^{-4}\) |
| Â | Â | \(1\times 10^{4}\) | 0.001 |
| Â | Â | \(1\times 10^{5}\) | 0.003 |
| Â | Â | \(1\times 10^{6}\) | 0.01 |
| Â | Â | \(1\times 10^{7}\) | 0.03 |
| Â | Â | \(1\times 10^{8}\) | 0.1 |