From: A multi-expert ensemble system for predicting Alzheimer transition using clinical features
Models | Hyperparameters | Range | |
---|---|---|---|
Ensemble proposed | Neural Net | Optimizer | {‘SGD’, ‘Adagrad’, ‘Adadelta’, ‘Adam’, ‘Adamax’, ‘Nadam’} |
Batch size | {10, 20, 40, 60, 80, 100} | ||
Epochs | {10, 50, 100} | ||
Number of hidden units | {2:2:50} | ||
Random Forest | Max depth | {5, 20, 50, 80, 110} | |
Min samples for leaf | {3, 4, 5, 10]} | ||
Min samples for split | {8, 10, 12, 24, 32} | ||
Number of estimators | {30, 200, 300, 1000} | ||
Support Vector Machine | C parameter | {0.1,1, 10, 100} | |
\(\gamma\) parameter | {1, 0.1, 0.01, 0.001} | ||
Kernel | {‘linear’, ‘poly’, ‘rbf’, ‘sigmoid’} |