0 ×

Deprecated**Decisions Tree Ensembles for KNIME** version **4.1.0.v201911271250** by **KNIME AG, Zurich, Switzerland**

Learns a single regression tree. The procedures 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, missing values ignored, not necessarily binary trees, etc.

- 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 to use learn the model. Two variants are possible.

*Fingerprint attribute*uses the different bit/count positions in the selected bit/byte vector as learning attributes (for instance a bit/byte vector of length 1024 is expanded to 1024 binary/count attributes). All bit/byte vectors in the selected column must have the same length.*Column attributes*are nominal and numeric columns used as descriptors. Numeric columns are split in a <= fashion; nominal columns are currently split by creating child nodes for each of the values.- 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.

- 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.

- 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.

- Partitioning (12 %)
- CSV Reader (9 %)
- Column Filter (9 %) Streamable
- File Reader (6 %) Streamable
~~Number To String~~(6 %) Deprecated- Missing Value Column Filter (6 %)
~~XLS Reader~~(6 %) Deprecated- Inject Variables (Data) (3 %)
- Table Reader (3 %) Streamable
- X-Partitioner (3 %)
~~PCA~~(3 %) Deprecated- Linear Regression Learner (3 %)
~~Regression Predictor~~(3 %) Deprecated- Auto-Binner (3 %)
~~String To Number~~(3 %) StreamableDeprecated- Row Splitter (3 %) Streamable
- GroupBy (3 %)
- Normalizer (3 %)
- Missing Value (3 %)
- Rule-based Row Filter (3 %) Streamable
- Color Manager (3 %)
- Boosting Learner Loop Start (3 %)
- Math Formula (3 %) Streamable
- Show all 23 recommendations

~~Simple Regression Tree Predictor~~(91 %) Deprecated- Model Loop End (3 %)
- Boosting Learner Loop End (3 %)
- Model to Cell (3 %)

To use this node in KNIME, install KNIME Core from the following update site:

KNIME 4.1

A zipped version of the software site can be downloaded here. Read our FAQs to get instructions about how to install nodes from a zipped update site.

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.

Do you have feedback, questions, comments about NodePit, want to support this platform, or want your own nodes or workflows listed here as well? Do you think, the search results could be improved or something is missing? Then please get in touch! Alternatively, you can send us an email to mail@nodepit.com, follow @NodePit on Twitter, or chat on Gitter!

Please note that this is only about NodePit. We do not provide general support for KNIME — please use the KNIME forums instead.