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. 2021 Jul 31;27(3):189–199. doi: 10.4258/hir.2021.27.3.189

Table 3.

Support vector machine (SVM) and random forest (RF) pipelines with their hyperparameters

SVM pipeline Hyperparameters RF pipeline Hyperparameters
SVM_Pipeline = {
 Imputation,
 Normalization,
 Standardization,
 Fs_elasticnet,
 SVM
}		 Imputation = {
 Strategy = ‘mean’
}
Normalization = {
 Feature_range = (0,1)
}
Standardisation = {
 with_mean = True,
 with_std =True
}
Fs_elasticnet = {
SelectFromModel {
ElasticNet {
 L1_ratio = 0.66,
 alpha = 1.0
}}}
SVM = {
 kernel=‘rbf ‘,
 gamma=‘scale’,
C=0.30000000000000004,
}		 RF_Pipeline = {
 Imputation,
 Normalization,
 Standardization,
 Fs_elasticnet,
 RF
}		 Imputation = {
 Strategy = ‘mean’
}
Normalization = {
 Feature_range = (0,1)
}
Standardization = {
 with_mean = True,
 with_std = True
}
Fs_elasticnet = {
SelectFromModel {
ElasticNet {
L1_ratio = 0.9,
alpha = 1.0
}}}
RF = {
n_estimators=100,
max_features=‘auto’,
min_samples_split=2,
min_samples_leaf=2,
}