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[P] Training Random Forest with a single vector (for each obs) in h2o?

I’m starting to use h2o to train and serve models. I have a dataset that I’d already curated for Spark ML pipelines. I have a single 16D vector I pass as the training data for each observation.

A friend said that h2o requires columns for each category and treats my single vector as a string, which I just can’t find anything to support. The accuracy is around what I got out of Spark ML, but I’m worried about how h2o is handling my data. Does anyone know how h2o handles this case?

tl;dr – Can I use a single vector for each training observation in h2o?

submitted by /u/Octosaurus
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Toronto AI is a social and collaborative hub to unite AI innovators of Toronto and surrounding areas. We explore AI technologies in digital art and music, healthcare, marketing, fintech, vr, robotics and more. Toronto AI was founded by Dave MacDonald and Patrick O'Mara.