This simple workflow provides an example on how to use the verified component “Conditional Density Plot”. A general question it helps to answer when trying to find a good predictor for Y from feature X: "Is X a good feature?" A part of this question can be answered by answering instead: "Does the distribution of X change by partitioning with class Y?". Because if it does, X might be good predictor for Y. If it does too well (i.e. 100%), data leakage might be present. Examples analysis is done on the titanic dataset. Each row of the data stands for a passenger, with columns like name, sex, age, booked class, fare, ... and whether he or she survived the incident. The task is to identify a feature that can act as a good predictor whether someone survived. Drag and drop the component in another workflow from its KNIME Hub page (link below) to visualize a different dataset. Apply your custom settings by opening the component dialogue. More information is available in the component description. Note that a Conditional Box Plot can be used for similar, less detailed analysis. The Conditional Density Plot offers more insight on the distribution itself.