IconRandom Forest Learner (Regression)0 ×

Decisions Tree Ensembles for KNIME version 3.6.0.v201807031236 by KNIME AG, Zurich, Switzerland

Learns a random forest (an ensemble of decision trees) for regression. Each of the regression 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 regression tree models and is applied in the corresponding predictor node using a simple mean of the individual predictions.

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

This node provides a subset of the functionality of the Tree Ensemble Learner (Regression). If you need additional functionality, please check out the Tree Ensemble Learner (Regression)

Options

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).

Enable Hightlighting (#patterns to store)
If selected, the node stores the selected number of rows and allows highlighting them in the node view.
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 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.
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.
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-vector/byte-vector) column or another numeric or nominal column.

Output Ports

The input data with the out-of-bag response estimates, i.e. for each input row the mean output 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 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 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.

Best Friends (Incoming)

Best Friends (Outgoing)

Update Site

To use this node in KNIME, install Decisions Tree Ensembles for KNIME from the following update site:

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