Open Access
Research Article

Table 1

Default and Bayesian optimized parameters for the random forest and gradient boosting algorithms, learning rate is not a hyperparameter of the random forest algorithm, minimum impurity decrease was also optimized but did not show a change from 0.0, variable max depth of the default random forest is defined as the expansion of tree until endpoints contain less than [min_samples_split] samples.

Algorithm default/optimized # Trees Max features per tree Min samples split Min samples leaf Max depth Learning rate
Random Forest 100/800 6/3 2/2 1/1 var./11 N.A.
Gradient Boost 100/490 6/3 2/20 1/8 3/5 0.1/0.02

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