Table 2 Model hyperparameter configurations of machine learning models
From: Prediction of small-molecule compound solubility in organic solvents by machine learning algorithms
Machine learning algorithms | Hyperparameter configurations |
|---|---|
lightGBM | 0.1503; 486; 0.5338; 0.2431; 32; 18 |
SVM | Radial basis function; 112.4; 0.0006046 |
RF | 486; 203; 45; 4; 2 |
ET | 396, 504, 39, 4, 2 |
kNN | 4; the standard Euclidean distance |
DT | 25; 8; 3 |
PLS | 22 |
DNN | 10; 1024, 256, 128, 64, 64, 64, 64, 64, 64, 64; 0.001; Adam with β1, β2 of 0.9, 0.999; 100; 500; 0.0003 |
Ridge regression | 1 |