PolyML has a suite of tools to analyze what is called Attribute Importance or Feature Importance (FI). The typical scenario involves using Machine Learning (ML) over some training data to construct predictors. These predictors take rows of attributes and produce a result which attempts to reproduce a particular output label (dependent variable). FI is a measure of how much each attribute contributes to produce the answer.
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