Tree Ensemble Learner

Learns an ensemble of decision trees (such as random forest* variants). Typically, each tree is built with a different set of rows (records) and/or columns (attributes). See the options for Data Sampling and Attribute Sampling for more details. The attributes can also be provided as bit (fingerprint), byte, or double vector. The output model describes an ensemble of decision tree models and is applied in the corresponding predictor node using the selected aggregation mode to aggregate the votes of the individual decision trees.

The following configuration settings learn a model that is similar to the random forest ™ classifier described by Leo Breiman and Adele Cutler:

• Tree Options - Split Criterion: Gini Index
• Tree Options - Limit number of levels (tree depth): unlimited
• Tree Options - Minimum node size: unlimited
• Ensemble Configuration - Number of models: Arbitrary (arguably, random forest does not overfit)
• Ensemble Configuration - Data Sampling: Use all rows (fraction = 1) but choose sampling with replacement (bootstrapping)
• Ensemble Configuration - Attribute Sampling: Sample using a different set of attributes for each tree node split; usually square root of number of attributes - but this can vary

Experiments have shown that results on different datasets are very similar to the random forest implementation available in R .

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 that for each split to try and send the missing values in every possible 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 the split that the most records are following is chosen as the direction for missing values during testing.

The tree ensemble nodes now also support binary splits for nominal columns. Depending on the kind of problem (two- or multi-class), different algorithms are implemented to enable the efficient calculation of splits.

• 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.

Input ports

1. Type: Table
   Input Data
   The data to be learned from. It must contain at least one nominal target column and either a fingerprint (bit/byte/double vector) column or another numeric or nominal column.

Output ports

1. Type: Table
   Out-of-bag Predictions
   The input data with the out-of-bag predictions, i.e. for each input row the majority vote of all models that did not use the row during their training. If the entire data was used to train the individual models then this output will contain the input data with missing values in the prediction columns. 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 voting (number of models not using the row throughout the learning.) The out-of-bag predictions can be used to get an estimate of the generalization error of the tree ensemble by feeding them into the Scorer node.
2. Type: Table
   Attribute Statistics
   A statistics table on the attributes used in the different trees. 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 is not split on level 1 because it has been split one level up), the #candidate number still counts the attribute as a candidate.
3. Type: Tree Ensembles
   Tree Ensemble Model
   The trained model.