h2o_predict_MOJO lets the user predict using the h2o .zip file
containing the MOJO files. Note that it works with the files
generated when using the function export_results()
h2o_predict_binary lets the user predict using the h2o binary file.
Note that it works with the files generated when using the
function export_results(). Recommendation: use the
h2o_predict_MOJO() function when possible - it let's you change
h2o's version without problem.
h2o_predict_model lets the user get scores from a H2O Model Object.
h2o_predict_API lets the user get the score from an API service
Usage
h2o_predict_MOJO(df, model_path, method = "mojo", batch = 300)
h2o_predict_binary(df, model_path, sample = NA)
h2o_predict_model(df, model)
h2o_predict_API(df, api, exclude = "tag")Arguments
- df
Dataframe/Vector. Data to insert into the model.
- model_path
Character. Relative model path directory or zip file.
- method
Character. One of "mojo" or "json".
- batch
Integer. Run n batches at a time for "json" method.
- sample
Integer. How many rows should the function predict?
- model
h2o model Object
- api
Character. API URL.
- exclude
Character. Name of the variables to exclude.
Value
data.frame with predicted results.
vector with predicted results.
data.frame with predicted results.
vector with predicted results.
See also
Other Machine Learning:
ROC(),
conf_mat(),
export_results(),
gain_lift(),
h2o_automl(),
h2o_selectmodel(),
impute(),
iter_seeds(),
lasso_vars(),
model_metrics(),
model_preprocess(),
msplit()