Simple Regression Tree Learner

Learns a single regression tree. The procedure follows the algorithm described by "Classification and Regression Trees" (Breiman et al, 1984), whereby the current implementation applies a couple of simplifications, e.g. no pruning, not necessarily binary trees, etc.

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.

The currently used missing value handling also differs from the one used by Breiman et al, 1984. In each split the algorithm tries to find the best direction for missing values by sending them in each direction and selecting the one that yields the best result (i.e. largest gain). The procedure is adapted from the well known XGBoost algorithm and is described here .

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

Ignore columns without domain information

If selected, nominal columns with no domain information are ignored (as they likely have too many possible values anyway).

Missing value handling

Specify how missing values should be handled during training.

XGBoost: If this is selected (it is also the default), the learner will calculate which direction is best suited for missing values, by sending the missing values in each direction of a split. The direction that yields the best result (i.e. largest gain) is then used as default direction for missing values. This method works with both, binary and multiway splits.
Surrogate: This approach calculates for each split alternative splits that best approximate the best split. The method was first described in the book "Classification and Regression Trees" by Breiman et al. (1984). NOTE: This method can only be used with binary nominal splits.

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)

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 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 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.

Enable highlighting

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

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 trained model.

Popular Predecessors

Popular Successors

Views

Regression Tree View: Regression Tree View

Workflows

Developers

You want to see the source code for this node? Click the following button and we’ll use our super-powers to find it for you.

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

Deploy, schedule, execute, and monitor your KNIME workflows locally, in the cloud or on-premises – with our brand new NodePit Runner.

Try NodePit Runner!