This node performs Target Shuffling by randomly permuting the values
in one column of the input table. This will break any connection
between input variables (learning columns) and response variable
(target column) while retaining the overall distribution of the
target variable. Target shuffling is used to estimate the baseline
performance of a predictive model. It's expected that the quality of
a model (accuracy, area under the curve, R², ...) will decrease
drastically if the target values were shuffled as any relationship
between input and target was removed.
It's advisable to repeat this process (target shuffling + model building + model evaluation) many times and record the bogus result in order to receive good estimates on how well the real model performs in comparison to randomized data.
Target shuffling is sometimes called randomization test or y-scrambling. For more information see also Handbook of Statistical Analysis and Data Mining Applications by Gary Miner, Robert Nisbet, John Elder IV.
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.
Deploy, schedule, execute, and monitor your KNIME workflows locally, in the cloud or on-premises – with our brand new NodePit Runner.Try NodePit Runner!
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.