Removes rows from the input data set such that the values in a categorical column are equally distributed. This can be useful, for instance if a learning algorithm is prone to unequal class distributions and you want to downsize the data set so that the class attributes occur equally often in the data set.
The node will remove random rows belonging to the majority classes. The rows returned by this node will contain all records from the minority class(es) and a random sample from each of the majority classes, whereby each sample contains as many objects as the minority class contains.
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.
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.