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|
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