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Why do Spark MLlib models only accept a vector column as input?
In other libraries I can just use the feature columns themselves as inputs, why do I need to make a vector out of my features when I use MLlib?
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Yeah, it's more a design choice. Rather than have every implementation take column(s) params, this is handled once in VectorAssembler for all of them. One way or the other, most implementations need a vector of inputs anyway. VectorAssembler can do s...
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