rm.operator.parallel_random_forest
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Uploaded 15-07-2016 by
Jan van Rijn
RapidMiner_6.4.0
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Parameters
| apply_prepruning | Activates the pre pruning and delivers a prepruned tree. | default: true |
| apply_pruning | Activates the pruning of the tree. | default: true |
| confidence | The confidence level used for the pessimistic error calculation of pruning. | default: 0.25 |
| criterion | Specifies the used criterion for selecting attributes and numerical splits. | default: gain_ratio |
| guess_subset_ratio | Indicates that log(m) + 1 features are used, otherwise a ratio has to be specified. | default: true |
| local_random_seed | Specifies the local random seed | default: 1992 |
| maximal_depth | The maximum tree depth (-1: no bound) | default: 20 |
| minimal_gain | The minimal gain which must be achieved in order to produce a split. | default: 0.1 |
| minimal_leaf_size | The minimal size of all leaves. | default: 2 |
| minimal_size_for_split | The minimal size of a node in order to allow a split. | default: 4 |
| number_of_prepruning_alternatives | The number of alternative nodes tried when prepruning would prevent a split. | default: 3 |
| number_of_trees | The number of learned random trees. | default: 10 |
| subset_ratio | Ratio of randomly chosen attributes to test | default: 0.2 |
| use_local_random_seed | Indicates if a local random seed should be used. | default: false |
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