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:
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 attributes (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 you 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).
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.
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:
A zipped version of the software site can be downloaded here.
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 email@example.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.