Re: Dense Vectors outputs in feature engineering

Since you use two steps (StringIndexer and OneHotEncoder) to encode categories to Vector, I guess you want to decode the eventual vector into their original categories. Suppose you have a DataFrame with only one column named "name", there are three categories: "b", "a", "c" (ranked by frequency).

Re: Dense Vectors outputs in feature engineering

or would it be common practice to just retain the original categories in another df? -- View this message in context: http://apache-spark-user-list.1001560.n3.nabble.com/Dense-Vectors-outputs-in-feature-engineering-tp27331p27337.html Sent from the Apache Spark User List mailing list archive at

Re: Dense Vectors outputs in feature engineering

Thanks Disha, that worked out well. Can you point me to an example of how to decode my feature vectors in the dataframe, back into their categories? -- View this message in context:

Re: Dense Vectors outputs in feature engineering

Hi Ian, You can create a dense vector of you features as follows: - String Index your features - Invoke One Hot Encoding on them, which generates a sparse vector - Now, in case you wish to merge these features, then use VectorAssembler (optional) - After transforming the dataframe to return