Classifying Data in Orange: What's the difference between 'Features', 'Target Variables', and 'Meta Attributes' I've been looking for a more visual way to calculate statics on my spectroscopy data.  Python's Orange Canvas seems like it might be a great alternative to Pipeline Pilot, but I'm having trouble getting started with more simplistic data analyses.  

Most notably, when I import data, I'm unclear as to what the difference between a 'Target Variable', 'Feature', and 'Meta Attribute' is.

How do I decide what type of data I have?
Meta variables are meta data, data about data, not used for statistical inference.
Features or variables or attributes are the measured inputs of the problem domain, the independent variables. The target variable is the dependent variable or the measure we're trying to model or forecast. Not all problems can be or need to be formulated in such a way. Orange was traditionally designed to accommodate machine learning workflows, hence the naming.