I have been using h2o's autoencoder (deeplearning with autoencoder=TRUE) and h2o.anomaly for anomaly detection. So far these have been performed on the entire dataset. However, now I need to build a separate model for each segment (subpopulation) of the dataset. I found h2o.train_segments so I tried it, but it gave me this error message:
Error in is.numeric(y) : argument "y" is missing, with no default
Does this mean that h2o.train_segments doesn't work for h2o's autoencoder?
Here is my code:
mod <- h2o.train_segments(algorithm = "deeplearning",
segment_columns = "some_col",
parallelism = 1,
training_frame = data.hex,
activation = "tanh",
standardize = TRUE,
hidden = c(10, 1, 10),
epochs = 5,
autoencoder = TRUE,
loss = "CrossEntropy",
seed = 42)
Thank you for your insights!