[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?