Random Forest Learner

Learns a random forest*, which consists of a chosen number of decision trees. Each of the decision tree models is built with a different set of rows (records) and for each split within a tree a randomly chosen set of columns (describing attributes) is used. The row sets for each decision tree are created by bootstrapping and have the same size as the original input table. The attribute set for an individual split in a decision tree is determined by randomly selecting sqrt(m) attributes from the available attributes where m is the total number of learning columns. The attributes can also be provided as bit (fingerprint), byte, or double vector. The output model describes a random forest and is applied in the corresponding predictor node.

This node provides a subset of the functionality of the Tree Ensemble Learner corresponding to a random forest. If you need additional functionality please check out the Tree Ensemble Learner.

Experiments have shown the results on different datasets 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 that for each split to try to send the missing values in every possible direction; 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.

Nominal columns are split in a binary manner. The determination of the split depends on the kind of problem:

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.

Options

Target column

Select the column containing the value to be learned. Rows with missing values in this column are ignored during the learning process.

Training attributes

Choose whether to derive attributes from a fingerprint vector column or from ordinary table columns.

Use column: Select ordinary columns and configure include/exclude rules for training attributes.
Use fingerprint: Expand a fingerprint (bit, byte, or double vector) column into individual attributes during training.

Fingerprint attribute

Use a fingerprint (bit, byte, or double vector) column to learn the model. Each entry of the vector is treated as a separate attribute. All vectors must share the same length.

Attribute selection

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; the information gain ratio normalizes the standard information gain by the split entropy to overcome any unfair preference for nominal splits with many child nodes.

Information Gain: Maximizes the information gain when creating splits. Equivalent to entropy reduction.
Information Gain Ratio: Uses the information gain ratio to compensate for the bias towards attributes with many distinct values.
Gini: Measures the impurity of a split based on the Gini index. Common default for classification tasks.

Limit number of levels (tree depth)

Limit the maximal number of tree levels. When disabled the tree depth is unbounded. For instance, a value of 1 would only split the (single) root node resulting in a decision stump

Minimum child node size

Minimum number of records allowed in the child nodes after a split. Must not exceed half the minimum split node size. Note, this parameter is currently ignored for nominal columns if binary nominal splits are disabled.

Number of models

Number of decision trees to learn. Larger ensembles generally provide more stable results but increase runtime. For most datasets, a value between 100 and 500 yields good results; however, the optimal number is data dependent and should thus be subject to hyperparameter tuning.

Use static random seed

Provide a seed to obtain deterministic results. Leave disabled to use a time-dependent seed.

Generate new seed

Generate a random seed and apply it to the field above for reproducible runs.

Enable highlighting

If selected, the node stores the configured number of rows and allows highlighting them in the node view.

Save target distribution in tree nodes

If selected, the model stores the distribution of the target category values in each tree node. Storing the class distribution may increase memory consumption considerably and we therefore recommend disabling it if your use-case doesn't require it. Class distribution is only needed if

You want to see the class distribution for each tree node in the node view.
You want to export individual decision trees to PMML.
You want to use soft-voting (i.e. aggregation of probability distributions instead of votes) in the predictor node.

Input Ports

: The data to be learned from. It must contain at least one nominal 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 predictions, i.e. for each input row this is the majority vote of all models that did not use the row during their training. 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 learning.)
: 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 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.

Popular Predecessors

Popular Successors

Views

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

Workflows

Developers

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Installation

To use this node in KNIME, install the extension KNIME Ensemble Learning Wrappers from the below update site following our NodePit Product and Node Installation Guide:

v5.10

A zipped version of the software site can be downloaded here.

Plugin provider: KNIME AG, Zurich, Switzerland

Plugin version: 5.10.0.v202601200824

On NodePit since: 2026-02-18

Last update: 2026-02-18

Tags: Modern UI

KNIME versions: Since v3.6

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