Tree Ensemble Learner (Regression)

Learns an ensemble of regression 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 regression tree models and is applied in the corresponding predictor node using a simple mean of the individual predictions.

In a regression tree the predicted value for a leaf node is the mean target value of the records within the leaf. Hence the predictions are best (with respect to the training data) if the variance of target values within a leaf is minimal. This is achieved by splits that minimize the sum of squared errors in their respective children.

For a more general description and suggested default parameters see the node description of the classification Tree Ensemble Learner.

(*) 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 are 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 a 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 you 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.

Tree Options

Use mid points splits (only for numeric attributes)
Uses the middle point between two class boundaries for numerical splits. 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 columns if binary nominal splits are disabled.
Use fixed root attribute
If selected, the chosen column is used as the root split attribute in all decision trees -- even if the column is not in the attribute sample.

Ensemble Configuration

Number of models
The number of regression trees to be learned. A "reasonable" value can range from very few (say 10) to many thousands, although a value between 100 and 500 suffices for most datasets.
Data Sampling (Rows)
The sampling of the data rows for each individual tree: If disabled, each tree learner gets the full dataset, 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 datasets this bootstrap sample contains about 2/3 different data rows from the input, some of which are replicated multiple times. Rows that are not used in the training of a tree are called out-of-bag (see below).
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 starting with the square root of number of attributes but also trying to double/half that number).
Attribute Selection

Use same set of attributes for each tree means 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. This option is used in random forests.

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

Input Ports

The data to learn from. It must contain at least one numeric 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 predictions, i.e. for each input row the mean and variance of outputs of all models that did not use the row for training. If the entire data was used to train the individual models then this output will contain the input data with missing response and response variance 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 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 ability of the random forest by feeding them into the Numeric Scorer node.
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 the 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.
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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