This node allows to calculate the information gain score for all features of a given dataset. The provided output is a table with all feature names in the dataset and an associated information gain score. An explanation of the information gain feature selection criterion can be found for example in “A Comparative Study on Feature Selection in Text Categorization”, Yiming Yang, Jan O. Pedersen, 1997.
The information gain measure is usually employed to select the best split in a tree node when building decision trees. This node allows to calculate the information gain values for a list of features and output it as a single list, so that the worth of a given features can be analyzed conveniently.
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.