DeprecatedDecisions Tree Ensembles for KNIME version 4.1.0.v201911271250 by KNIME AG, Zurich, Switzerland
Learns a random forest*, which consists of a chosen number of decision trees. Each of the decision tree models is learned on a different set of rows (records) and a different set of columns (describing attributes), whereby the latter can also be a bit-vector or byte-vector descriptor (e.g. molecular fingerprint). The row sets for each decision tree are created by bootstrapping and have the same size as the original input table. For each node of a decision tree a new set of attributes is determined by taking a random sample of size sqrt(m) where m is the total number of attributes. The output model describes a random forest and is applied in the corresponding predictor node using a simply majority vote.
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 data sets are very similar to the random Forest implementation available in R. Known differences are in the missing value handling (currently not available in this node) and the split creation for nominal attributes (the original random forest classifier uses binary nominal splits; this implementation creates child nodes for each possible split attribute).
The decision tree construction takes place in main memory (all data and all models are kept in memory).
Select the attributes to use learn the model. Two variants are possible.
Fingerprint attribute uses the different bit/byte positions in the selected bit/byte vector as learning attributes (for instance a bit vector of length 1024 is expanded to 1024 binary attributes or 1024 long byte vector is expanded to the corresponding number of numeric attributes). All 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.
To use this node in KNIME, install KNIME Core from the following 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 firstname.lastname@example.org, 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.