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Table 2 Hyper-parameters of baseline methods

From: Disease risk analysis for schizophrenia patients by an automatic AHP framework

Methods

Hyper-parameters

Random Forest

num of trees: 1000, num of attr consider at each split: 5

Neural Network

Neurons of hidden layers: 100, activation: Relu, solver: Adam, regularization, learning rate: 0.001, iters: 200

Logistic Regression

regularization type: ridge(L2), strength: C = 1

SGD

Loss function: logistic regression, regularization method: Elastic Net, \(\epsilon\): 0.1, iters:1000

kNN

K: 9, metric: Euclidean, weight: Uniform

SVM

RBF, Kernel:\(exp(-g|x-y{|}^{2})\), C: 1.00,: 0.1, iteration limit: 100