Queries regarding feature importance for categorical features
From what I understand, you have 185 features and out of them you want to choose the 80 most informative/ important?

It really seems like the main focus of all algorithms for decision trees, which is how to
decide which attribute is the best for a split? I suggest you do some reading in the context of decision trees. For example: DECISION TREES - Presentation

What you are looking for is an Impurity-Measure which can help determine the Goodness-of-Split due to some discrete attribute (basically measuring information gain):



There are 3 measures which are used most commonly - Gini index, miss-classification error and entropy.