Table 2 Hyper-parameters tuning options available in QSAR-Co-X toolkit
From: QSAR-Co-X: an open source toolkit for multitarget QSAR modelling
| Technique | Parameters tuninga |
|---|---|
| RF | Bootstrap: True/ Falseb |
| Criterion: Gini, Entropy, | |
| Maximum depth: 10, 30, 50, 70, 90, 100, 200, None | |
| Maximum features: Auto, Sqrt | |
| Minimum samples leaf: 1, 2, 4 | |
| Minimum samples split: 2, 5, 10 | |
| Number of estimators: 50, 100, 200,500 | |
| kNN | Number of neighbours: 1–50 |
| Weight options: Uniform, Distance | |
| Algorithms: Auto, Ball tree, kd_tree, brute | |
| Bernoulli NB | Alpha:1, 0.5, 0.1 |
| Fit_prior: True, False | |
| SVC | C: 0.1, 1, 10, 100, 1000 |
| Gamma: 1, 0.1, 0.01, 0.001 | |
| Kernel: RBF, Linear, Poly, Sigmoid | |
| MLP | Hidden layer sizes: To be specified by the user |
| Activation: Identity, Logistic, Tanh, Relu | |
| Solver: SGD, Adam | |
| Alpha: 0.0001, 0.001, 0.01, 1 | |
| Learning rate: Constant, Adaptive, Invscaling | |
| GB | Loss: deviance, exponential |
| Learning rate: 0.01, 0.05, 0.1, 0.2 | |
| Min samples split: 0.1,0.2,0.3,0.4,0.5 | |
| Minimum samples leaf: 0.1,0.2,0.3,0.4,0.5 | |
| Maximum depth: 3,5,8 | |
| Maximum features: Log2, Sqrt | |
| Criterion: Friedman MSE, MAE | |
| Subsample: 0.5, 0.6, 0.8 | |
| Number of estimators: 50,100,200,300 |