Table 4 Hyperparameters in proposed machine learning models.
From: Refining skin lesions classification performance using geometric features of superpixels
Model | Hyperparameters |
|---|---|
RF | n_estimators = 100, *, criterion = 'entropy' |
SVM | kernel = 'liniar', degree = 2, gamma = 'scale', cache_size = 100, decision_function_shape = 'ovo' |
AD | n_estimators = 100 algorithm = 'SAMME', random_state = 40 |
KNN | n_neighbors = 1, *, weights = 'uniform', algorithm = ‘kd_tree’, leaf_size = 20, p = 2, metric = 'euclidean' |
DT | criterion = 'entropy', splitter = 'best', max_depth = 100, ccp_alpha = 0.0 |
GNB | priors = None, var_smoothing = 1e-09 |
