Table 5 The default parameters of the machine learning models used for flood risk simulations
From: Simulating flood risk in Tampa Bay using a machine learning driven approach
Model name | Description of parameters |
|---|---|
DT | criterion = gini, splitter = best, max_depth = None, max_features = None, random_state = 42 |
SVM | C = 1.0, kernel = rbf; degree = 3, gamma = scale, probability = True, tolerance = 0.001, random_state = 42 |
AdaBoost | n_estimators = 50, learning_rate = 1.0, algorithm = SAMME.R, random_state = 42 |
XGBoost | n_estimators = 100, learning_rate = 0.3, max_depth = 6, gamma = 0, booster: gbtree, random_state = 42 |
RF | n_estimators = 100, criterion = gini, max_depth = None, max_features = sqrt random_state = 42 |
