| RandomForest__apply_prepruning | Activates the pre pruning and delivers a prepruned tree. | default: true |
| RandomForest__apply_pruning | Activates the pruning of the tree. | default: true |
| RandomForest__confidence | The confidence level used for the pessimistic error calculation of pruning. | default: 0.25 |
| RandomForest__criterion | Specifies the used criterion for selecting attributes and numerical splits. | default: gain_ratio |
| RandomForest__enable_parallel_execution | This parameter enables the parallel execution of this operator. Please disable the parallel execution if you run into memory problems. | default: true |
| RandomForest__guess_subset_ratio | Indicates that log(m) + 1 features are used, otherwise a ratio has to be specified. | default: true |
| RandomForest__local_random_seed | Specifies the local random seed | default: 1992 |
| RandomForest__maximal_depth | The maximum tree depth (-1: no bound) | default: 20 |
| RandomForest__minimal_gain | The minimal gain which must be achieved in order to produce a split. | default: 0.1 |
| RandomForest__minimal_leaf_size | The minimal size of all leaves. | default: 2 |
| RandomForest__minimal_size_for_split | The minimal size of a node in order to allow a split. | default: 4 |
| RandomForest__number_of_prepruning_alternatives | The number of alternative nodes tried when prepruning would prevent a split. | default: 3 |
| RandomForest__number_of_trees | The number of learned random trees. | default: 10 |
| RandomForest__random_splits | Split numerical attributes randomly. | default: false |
| RandomForest__subset_ratio | Ratio of randomly chosen attributes to test | default: 0.2 |
| RandomForest__use_local_random_seed | Indicates if a local random seed should be used. | default: false |
| RandomForest__voting_strategy | Voting strategy used to determine prediction. | default: confidence vote |