Methods | Hyperparameters | Definition | Value |
---|---|---|---|
ANN | Size | The number of nodes in the hidden layer | 5 |
Weight decay | The regularization parameter to avoid overfitting | 0.1 | |
SVM | Gamma | The width of the radial basis function kernel | 0.12 |
Cost | The parameter that controls the complexity of the model | 1 | |
RF | mtry | Number of variables randomly selected as candidates for each tree | 2 |
ntree | The number of trees | 500 | |
DT | minsplit | The minimum number of observations in a node | 10 |
minbucket | The minimum number of observations in any terminal node | 3 | |
XGBoost | nrounds | The maximum number of iterations | 100 |
eta | Learning rate | 0.3 | |
gamma | Regularization parameter to prevent overfitting | 5 | |
max depth | The depth of the tree | 3 |