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Table 4 Hyperparameter tuning analysis

From: Enhanced interpretable thyroid disease diagnosis by leveraging synthetic oversampling and machine learning models

Technique

Hyperparameter description

LR

random_state=0, max_iter=300, multi_class=‘auto’, C=1.0

LSVM

random_state=0, max_iter=500, multi_class=‘auto’, C=1.0

RF

n_estimators=200, max_depth=200, random_state=0

LGBM

n_estimators=300, boosting_type=‘gbdt’, num_leaves=31, importance_type=‘split’

GRU

loss = ‘binary_crossentropy’, activation=‘sigmoid’, metrics=‘accuracy’, optimizer = ‘adam’, epochs=20

LSTM

loss = ‘binary_crossentropy’, activation=‘sigmoid’, metrics=‘accuracy’, optimizer = ‘adam’, epochs=20

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BMC Medical Informatics and Decision Making

ISSN: 1472-6947

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