Machine learning framework with feature selection approaches for thyroid disease classification and associated risk factors identification

Journal of Electrical Systems and Information Technology

Table 3 Different parameters of algorithms used in this study

Algorithm	Parameters value
KNN	n_neighbors = 5
RF	Default
DT	max_depth = 1, criterion = 'gini', random_state = 1
SVM	Default
AB	base_estimator = dtclf, n_estimators = 50, learning_rate = 0.5, algorithm = 'SAMME.R', random_state = 1
GB	learning_rate = 0.1