R/models.R
h2o.genericModel.Rd
Usage example: generic_model <- h2o.genericModel(model_file_path = "/path/to/mojo.zip") predictions <- h2o.predict(generic_model, dataset)
h2o.genericModel(mojo_file_path, model_id = NULL)
mojo_file_path | Filesystem path to the model imported |
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model_id | Model ID, default is NULL |
Returns H2O Generic Model based on given embedded model
# NOT RUN { # Import default Iris dataset as H2O frame data <- as.h2o(iris) # Train a very simple GBM model features <- c("Sepal.Length", "Sepal.Length", "Sepal.Width", "Petal.Length", "Petal.Width") original_model <- h2o.gbm(x = features, y = "Species", training_frame = data) # Download the trained GBM model as MOJO (temporary directory used in this example) mojo_original_name <- h2o.download_mojo(model = original_model, path = tempdir()) mojo_original_path <- paste0(tempdir(), "/", mojo_original_name) # Import the MOJO as Generic model generic_model <- h2o.genericModel(mojo_original_path) # Perform scoring with the generic model generic_model_predictions <- h2o.predict(generic_model, data) # }