Table 1 Hyperparameters of the algorithms and their values
From: A robust and consistent stack generalized ensemble-learning framework for image segmentation
Algorithm | Hyperparameters | Meanings | Value |
|---|---|---|---|
XGBoost | n_estimators | Number of trees | 1000 |
learning_rate | Shrinkage coefficient of each tree | 0.2 | |
max_depth | Maximum depth of a tree | 50 | |
colsample_bytree | Subsample ratio of columns for tree construction | 1 | |
subsample | Subsample ratio of training samples | 0.3 | |
LightGBM | n_estimators | Number of trees | 1900 |
learning_rate | Shrinkage coefficient of each tree | 0.2 | |
max_depth | Maximum depth of a tree | 80 | |
num_leaves | Number of leaves for each tree | 11 | |
SVM | max_iter | Hard limit on iterations within solver | 100 |
C | Regularization parameter | 1.2 |