I am new to Pyspark. I have a dataset that contains categorical features and I want to use regression models from pyspark to predict continuous values. I am stuck in pre-processing of data that is required for using MLlib models.
Is it necessary to convert categorical attributes to numerical attributes to use LabeledPoint function in Pyspark?
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Yes, it is necessary. You have to not only convert to numerical but also encode to make them useful for linear models. Both steps are implemented in
pyspark.ml(notmllib) with:pyspark.ml.feature.StringIndexer- indexing.pyspark.ml.feature.OneHotEncoder- encoding.