Tree Ensemble Learner

This Node Is Deprecated — This version of the node has been replaced with a new and improved version. The old version is kept for backwards-compatibility, but for all new workflows we suggest to use the version linked below.
Go to Suggested ReplacementTree Ensemble Learner

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
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.

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.


Attribute Selection

Target Column
Select the column containing the value to be learned. Rows with missing values in this column will be ignored during the learning process.
Attribute Selection

Select the attributes on which the model should be learned. You can choose from two modes.

Fingerprint attribute Uses a fingerprint/vector (bit, byte and double are possible) column to learn the model by treating each entry of the vector as separate attribute (e.g. a bit vector of length 1024 is expanded into 1024 binary attributes). The node requires all vectors to be of the same length.

Column attributes Uses ordinary columns in your table (e.g. String, Double, Integer, etc.) as attributes to learn the model on. The dialog allows to select the columns manually (by moving them to the right panel) or via a wildcard/regex selection (all columns whose names match the wildcard/regex are used for learning). In case of manual selection, the behavior for new columns (i.e. that are not available at the time you configure the node) can be specified as either Enforce exclusion (new columns are excluded and therefore not used for learning) or Enforce inclusion (new columns are included and therefore used for learning).

Ignore columns without domain information
If selected, nominal columns with no domain information are ignored (as they likely have too many possible values anyway).
Enable Hightlighting (#patterns to store)
If selected, the node stores the selected number of rows and allows highlighting them in the node view.
Save target distribution in tree nodes (memory expensive - only important for tree view and PMML export)
If selected, the model will store the distribution of the target category values in each tree node. This information is not relevant for the prediction but is accessible via the tree view or when exporting individual models to PMML. Enabling this option may increase memory consumption considerably.

Tree Options

Split Criterion
Choose the split criterion here. Gini is usually a good choice and is used in "Classification and Regression Trees" (Breiman et al, 1984) and the original random forest algorithm (as described by Breiman et al, 2001); information gain is used in C4.5; information gain ratio normalizes the standard information gain by the split entropy to overcome some unfair preference for nominal splits with many child nodes.
Use mid points splits (only for numeric attributes)
Uses for numerical splits the middle point between two class boundaries. If unselected the split attribute value is the smaller value with "<=" relationship.
Use binary splits for nominal columns
If selected, nominal columns also produce binary splits instead of multiway splits in which each nominal value corresponds to one child node.
Limit number of levels (tree depth)
Number of tree levels to be learned. For instance, a value of 1 would only split the (single) root node (decision stump).
Minimum split node size
Minimum number of records in a decision tree node so that another split is attempted. Note, this option does not make any implications on the minimum number of records in a terminal node. If enabled, this number needs to be at least twice as large as the minimum child node size (as otherwise for binary splits one of the two children would have less records than specified).
Minimum child node size
Minimum number of records in child nodes. It can be at most half of the minimum split node size (see above). Note, this parameter is currently ignored for nominal splits.
Use fixed root attribute
If selected the chosen column will be used as root split attribute in all decision trees -- even if the column is not in the attribute sample (see below).

Ensemble Configuration

Number of models
The number of decision trees to learn. A "reasonable" value can range from very few (say 10) to many thousands for small data sets with few target category values.
Data Sampling (Rows)
The sampling of the data rows for each individual tree: If disabled each tree learner gets the full data set, otherwise each tree is learned with a different data sample. A data fraction of 1 (=100%) chosen "with replacement" is called bootstrapping. For sufficiently large data sets this bootstrap sample contains about 2/3 different data rows from the input, some of which replicated multiple times. Rows which are not used in the training of a tree are called out-of-bag (see below).
Data Sampling Mode
The sampling mode decides how the rows are sampled. In the random mode the rows are sampled from the whole data set i.e. each row has exactly the same probability to be in the sample. In case of equal size sampling, first a sample from the minority class is drawn and then the same number of rows as in the minority sample are drawn from all other classes i.e. each class is represented with the same number of rows in the sample. If stratified sampling is selected, the same fraction of rows are drawn from each class i.e. the class distribution in the sample is approximately the same as in the full set of rows.
Attribute Sampling (Columns)
Defines the sampling of attributes to learn an individual tree. This can either be a function based on the number of attributes (linear fraction or square root) or some absolute value. The latter can be used in conjunction with flow variables to inject some other value derived from the number of attributes (e.g. Breiman suggests to start with the square root of number of attributes but also try to double/half that number).
Attribute Selection

Use same set of attributes for each tree describes that the attributes are sampled once for each tree and this sample is then used to construct the tree.

Use different set of attributes for each tree node samples a different set of candidate attributes in each of the tree nodes from which the optimal one is chosen to perform the split.

Use static random seed
Choose a seed to get reproducible results.

Input Ports

The data to learn 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

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.


Tree Views
An decision tree viewer for all the trained models. Use the spinner to iterate through the different models.




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