Logistic Regression |
{penalty, solver, C, max_iter} |
{‘l2’, ‘liblinear’, ‘1.0’, ‘100’} |
Random Forest |
{criterion, n_estimators} |
{‘gini’, ‘100’} |
Gradient Boosting |
{criterion, n_estimators, learning_rate} |
{‘friedman_mse’, ‘100’,’0.1’} |
XGBoost |
{booster, gamma, n_estimators, learning_rate} |
{‘gbtree’, ‘1’, ‘100’,’0.1’} |
Deep Neural Network |
{epoch, batch_size, activation, loss, network layer} |
{‘300’, ‘100’, ‘relu’, ‘binary_crossentropy’, [12-50-50-50-1]} |
1D-CNN |
{epoch, batch_size, activation, loss, network layer} |
{‘300’, ‘100’, ‘relu’, ‘categorical_crossentropy, [25–32–32–64–64–64–64–2]} |