Learns a random forest*, which consists of a chosen number of decision trees. Each of the decision tree models is learned on a different set of rows (records) and a different set of columns (describing attributes), whereby the latter can also be a bit-vector or byte-vector descriptor (e.g. molecular fingerprint). The row sets for each decision tree are created by bootstrapping and have the same size as the original input table. For each node of a decision tree a new set of attributes is determined by taking a random sample of size sqrt(m) where m is the total number of attributes. The output model describes a random forest and is applied in the corresponding predictor node using a simple majority vote.
This node provides a subset of the functionality of the Tree Ensemble Learner corresponding to a random forest. If you need additional functionality please check out the Tree Ensemble Learner.
Experiments have shown the results on different data sets are very similar to the random Forest implementation available in R.
The decision tree construction takes place in main memory (all data and all models are kept in memory).
The missing value handling corresponds to the method described here. The basic idea is to try for each split to send the missing values in every direction and the one yielding the best results (i.e. largest gain) is then used. If no missing values are present during training, the direction of a split that the most records are following is chosen as direction for missing values during testing.
Nominal columns are split in a binary manner. The determination of the split depends on the kind of problem:
- For two-class classification problems the method described in section 9.4 of "Classification and Regression Trees" by Breiman et al. (1984) is used.
- For multi-class classification problems the method described in "Partitioning Nominal Attributes in Decision Trees" by Coppersmith et al. (1999) is used.
(*) RANDOM FORESTS is a registered trademark of Minitab, LLC and is used with Minitab’s permission.
Options
Input Ports
- The data to learn from. It must contain at least one nominal target column and either a fingerprint (bit-vector/byte-vector) column or another numeric or nominal column.
Output Ports
- The input data with the out-of-bag error estimates, i.e. for each input row the majority vote of all models that did not use the row in the training. If the entire data was used to train the individual models then this output will contain the input data with missing prediction and confidence values. The appended columns are equivalent to the columns appended by the corresponding predictor node. There is one additional column model count, which contains the number of models used for the voting (number of models not using the row throughout the learning.)
- A statistics table on the attributes used in the different tree learners. Each row represents one training attribute with these statistics: #splits (level x) as the number of models, which use the attribute as split on level x (with level 0 as root split); #candidates (level x) is the number of times an attribute was in the attribute sample for level x (in a random forest setup these samples differ from node to node). If no attribute sampling is used #candidates is the number of models. Note, these numbers are uncorrected, i.e. if an attribute is selected on level 0 but is also in the candidate set of level 1 (but will not be split on level 1 because it has been split one level up), the #candidate number will still count the attribute as candidate.
- The trained model.
Views
- Tree Views
- An decision tree viewer for all the trained models. Use the spinner to iterate through the different models.
Workflows
Links
- No links available
Developers
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