lares (version 5.1.0)

h2o_predict_MOJO: H2O Predict using MOJO file

Description

This function 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()

Usage

h2o_predict_MOJO(df, model_path, method = "mojo", batch = 300)

Arguments

df

Dataframe. Data to pass to the model.

model_path

Character. Relative path of directory where your zip model file is. If multiple zip files are found, first one found will be used.

method

Character. One of "mojo" or "json".

batch

Integer. Run n batches at a time for "json" method.

Value

data.frame with predicted results.

See Also

Other Machine Learning: ROC(), conf_mat(), export_results(), gain_lift(), h2o_automl(), h2o_predict_API(), h2o_predict_binary(), h2o_predict_model(), h2o_selectmodel(), impute(), iter_seeds(), lasso_vars(), model_metrics(), model_preprocess(), msplit()

Other Tools: autoline(), bind_files(), bring_api(), db_download(), db_upload(), export_plot(), export_results(), get_credentials(), h2o_predict_API(), h2o_predict_binary(), h2o_predict_model(), h2o_selectmodel(), haveInternet(), image_metadata(), importxlsx(), ip_data(), json2vector(), listfiles(), mail_send(), msplit(), myip(), quiet(), read.file(), statusbar(), tic(), try_require(), updateLares(), zerovar()