Table 2 Hyperparameter Tuning of Different Applied Models.
From: A novel hybrid approach for multi stage kidney cancer diagnosis using RCC ProbNet
Technique | Hyperparameter |
|---|---|
RF | n_estimators=300, max_depth=300, random_state=0, criterion=’gini’ |
LR | penalty=’l2’, C=1.0, solver=’lbfgs’, max_iter=100 |
GNB | var_smoothing=1e-9 |
SGD | loss=’log’, learning_rate=’optimal’, max_iter=1000, tol=1e-3, random_state=0 |
KNC | n_neighbors=5, weights=’uniform’, algorithm=’auto’ |
CNN | epochs=10, batch_size=32, optimizer=’adam’, loss=’categorical_crossentropy’ |
\(VGG-19\) | epochs=10, batch_size=32, optimizer=’adam’, loss=’categorical_crossentropy’, input_shape=(224,224,3) |
