Table 4 Settings and parameters for classification methods
From: Analysis of disease comorbidity patterns in a large-scale China population
Methods | Setting |
|---|---|
LR | using L2 regularization norm regularization intensity = 1 |
SVM | using the linear kernel function penalty parameter of the error term = 10 |
RF | Decision tree = 180 Bootstrap Sample oob_score = true Feature = Gini coefficient |
NN | Using multilayer feedforward neural network learning rate = 0.001 maximum number of iterations = 200 two hidden layers randomly optimizing the size of mini batches |