h2o.genericModel: Imports a model under given path, creating a Generic model...

In h2o: R Interface for the 'H2O' Scalable Machine Learning Platform

Imports a model under given path, creating a Generic model with it.

Description

Usage example: generic_model <- h2o.genericModel(model_file_path = "/path/to/mojo.zip") predictions <- h2o.predict(generic_model, dataset)

Usage

h2o.genericModel(mojo_file_path, model_id = NULL)

Arguments

mojo_file_path	Filesystem path to the model imported
model_id	Model ID, default is NULL

Value

Returns H2O Generic Model based on given embedded model

Examples

## 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)

## End(Not run)

h2o documentation built on May 29, 2024, 4:26 a.m.