Feature Elimination with Naive Bayes

This workflow demonstrates the usage of the Feature Elimination Meta Node. The first input is labeled training data, the second test data which does not need to be labeled.
Inside the meta node at least a learner and a predictor node need to be inserted between the loop start and the loop end node. Alternatively a partitioning node or even a cross validation meta node can be used.
The loop iterates over all columns and iteratively removes the attribute that has the lowest influence on classification accuracy.
After the loop has been finished the filter node can be used to filter out all attributes that do not affect classification accuracy much.

Feature Elimination with Naive Bayes

 

Resources

EXAMPLES Server: 04_Analytics/01_Preprocessing/01_Feature_Elimination_with_Naive_Bayes04_Analytics/01_Preprocessing/01_Feature_Elimination_with_Naive_Bayes*
Download a zip-archive

 

 


* Find more about the Examples Server here.
The link will open the workflow directly in KNIME Analytics Platform (requirements: Windows; KNIME Analytics Platform must be installed with the Installer version 3.2.0 or higher). In other cases, please use the link to a zip-archive or open the provided path manually