Learns an ensemble of decision trees (such as random forest* variants). Each of the decision tree models is learned on a different set of rows (records) and/or a different set of columns (describing attributes), whereby the latter can also be a bit/byte/double-vector descriptor (e.g. molecular fingerprint). 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 (random forest arguably 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 can vary
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.
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.