h2o.mojo_predict_df

Provides the method h2o.mojo_predict_df with which you can predict a MOJO model from R.

R interface for 'H2O', the scalable open source machine learning
platform that offers parallelized implementations of many supervised and
unsupervised machine learning algorithms such as Generalized Linear
Models (GLM), Gradient Boosting Machines (including XGBoost), Random Forests,
Deep Neural Networks (Deep Learning), Stacked Ensembles, Naive Bayes,
Generalized Additive Models (GAM), ANOVA GLM, Cox Proportional Hazards, K-Means, PCA, ModelSelection,
Word2Vec, as well as a fully automatic machine learning algorithm (H2O AutoML).

Erin LeDell

R Interface for the 'H2O' Scalable Machine Learning Platform

Navdeep Gill

Spencer Aiello

Anqi Fu

Arno Candel

Cliff Click

Tom Kraljevic

Tomas Nykodym

Patrick Aboyoun

Michal Kurka

Michal Malohlava

Ludi Rehak

Eric Eckstrand

Brandon Hill

Sebastian Vidrio

Surekha Jadhawani

Amy Wang

Raymond Peck

Wendy Wong

Jan Gorecki

Matt Dowle

Yuan Tang

Lauren DiPerna

Tomas Fryda

Veronika Maurerova

H2O.ai 

h2o.mojo_predict_df function

<dl><dt>frame</dt>
<dd>data.frame to score.</dd>
<dt>mojo_zip_path</dt>
<dd>Path to MOJO zip downloaded from H2O.</dd>
<dt>genmodel_jar_path</dt>
<dd>Optional, path to genmodel jar file. If NULL (default) then the h2o-genmodel.jar in the same folder as the MOJO zip will be used.</dd>
<dt>classpath</dt>
<dd>Optional, specifies custom user defined classpath which will be used when scoring. If NULL (default) then the default classpath for this MOJO model will be used.</dd>
<dt>java_options</dt>
<dd>Optional, custom user defined options for Java. By default '-Xmx4g -XX:ReservedCodeCacheSize=256m' is used.</dd>
<dt>verbose</dt>
<dd>Optional, if TRUE, then additional debug information will be printed. FALSE by default.</dd>
<dt>setInvNumNA</dt>
<dd>Optional, if TRUE, then then for an string that cannot be parsed into a number an N/A value will be produced, if false the command will fail. FALSE by default.</dd></dl>

Arguments

H2O Prediction from R without having H2O running — h2o.mojo_predict_df

<dl>

<dt>frame</dt>
<dd>data.frame to score.</dd>


<dt>mojo_zip_path</dt>
<dd>Path to MOJO zip downloaded from H2O.</dd>


<dt>genmodel_jar_path</dt>
<dd>Optional, path to genmodel jar file. If NULL (default) then the h2o-genmodel.jar in the same folder as the MOJO zip will be used.</dd>


<dt>classpath</dt>
<dd>Optional, specifies custom user defined classpath which will be used when scoring. If NULL (default) then the default classpath for this MOJO model will be used.</dd>


<dt>java_options</dt>
<dd>Optional, custom user defined options for Java. By default '-Xmx4g -XX:ReservedCodeCacheSize=256m' is used.</dd>


<dt>verbose</dt>
<dd>Optional, if TRUE, then additional debug information will be printed. FALSE by default.</dd>


<dt>setInvNumNA</dt>
<dd>Optional, if TRUE, then then for an string that cannot be parsed into a number an N/A value will be produced, if false the command will fail. FALSE by default.</dd>

</dl>

H2O Prediction from R without having H2O running

h2o.mojo_predict_df: H2O Prediction from R without having H2O running

Description

Usage

Value

Arguments