Optimal Binning

For feature selection/ elemination purposes, this component calculates IV(Information Values) for optimal categories of variables. This component also calculates WOE (Weight of Evidence) of categorized variables.




Step By Step Guide:

1- Initially, to run this component one should install Python Integration extensions.
2- For obtain a better Python node performance, pyarrow library should be installed.
3- Having installed pyarrow library, select serialization library as Apache Arrow under preferences. This option makes a huge difference as performance compared to Flatbuffers Column Serialization.
4- Then, specify desired IV threshold, target (label) and its bad category from dialog window. Target should be a string form to run this component.

Options

Specify Information Value Threshold

Enter Description

Specify the Target and Bad Category Respectively

The First dropdown is used for selecting the target, and the second is used for bad category selection.

Nodes

• Reference Column Filter3 ×
• Column Filter2 ×
• Column Rename2 ×
• Rule Engine2 ×
• Table Row to Variable2 ×
• Column Appender1 ×
• Column Auto Type Cast1 ×
• Column Rename (Regex)1 ×
• Column Resorter1 ×
• Component Input1 ×
• Component Output1 ×
• Concatenate1 ×
• Double Configuration1 ×
• Extract Column Header1 ×
• GroupBy1 ×
• Loop End1 ×
• Math Formula1 ×
• Numeric Row Splitter1 ×
• Python Script (legacy)1 ×
• String To Number1 ×
• Table Row to Variable Loop Start1 ×
• Timer Info1 ×
• Transpose1 ×
• Value Selection1 ×
• Variable to Table Column1 ×
• Variable to Table Row1 ×

Extensions

• FeatureKNIME Base nodes
• FeatureKNIME Javasnippet
• FeatureKNIME Math Expression (JEP)
• FeatureKNIME Python 2 Integration (legacy)
• FeatureKNIME Quick Forms

Links

• No links available