weka.RandomTree
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Uploaded 05-12-2016 by
J B
Weka_3.9.0
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Weka implementation of RandomTree
Parameters
| -do-not-check-capabilities | If set, classifier capabilities are not checked before classifier is built
(use with caution). | |
| B | Break ties randomly when several attributes look equally good. | |
| K | Number of attributes to randomly investigate. (default 0)
(<1 = int(log_2(#predictors)+1)). | default: 0 |
| M | Set minimum number of instances per leaf.
(default 1) | default: 1.0 |
| N | Number of folds for backfitting (default 0, no backfitting). | |
| S | Seed for random number generator.
(default 1) | default: 1 |
| U | Allow unclassified instances. | |
| V | Set minimum numeric class variance proportion
of train variance for split (default 1e-3). | default: 0.001 |
| batch-size | The desired batch size for batch prediction (default 100). | |
| depth | The maximum depth of the tree, 0 for unlimited.
(default 0) | |
| num-decimal-places | The number of decimal places for the output of numbers in the model (default 2). | |
| output-debug-info | If set, classifier is run in debug mode and
may output additional info to the console | |
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