ml_logistic_regression_tidiers

tidy.ml_model_logistic_regression

augment.ml_model_logistic_regression

augment._ml_model_logistic_regression

glance.ml_model_logistic_regression

These methods summarize the results of Spark ML models into tidy forms.

R interface to Apache Spark, a fast and general
engine for big data processing, see <https://spark.apache.org/>. This
package supports connecting to local and remote Apache Spark clusters,
provides a 'dplyr' compatible back-end, and provides an interface to
Spark's built-in machine learning algorithms.

Edgar Ruiz

sparklyr

R Interface to Apache Spark

Javier Luraschi

Kevin Kuo

Kevin Ushey

JJ Allaire

Samuel Macedo

Hossein Falaki

Lu Wang

Andy Zhang

Yitao Li

Jozef Hajnala

Maciej Szymkiewicz

Wil Davis

 RStudio

 The Apache Software Foundation

ml_logistic_regression_tidiers function

<dl><dt>x</dt>
<dd>a Spark ML model.</dd>
<dt>...</dt>
<dd>extra arguments (not used.)</dd>
<dt>newdata</dt>
<dd>a tbl_spark of new data to use for prediction.</dd>
<dt>new_data</dt>
<dd>a tbl_spark of new data to use for prediction.</dd></dl>

Arguments

Tidying methods for Spark ML Logistic Regression — ml_logistic_regression_tidiers

<dl>

<dt>x</dt>
<dd>a Spark ML model.</dd>


<dt>...</dt>
<dd>extra arguments (not used.)</dd>


<dt>newdata</dt>
<dd>a tbl_spark of new data to use for prediction.</dd>


<dt>new_data</dt>
<dd>a tbl_spark of new data to use for prediction.</dd>

</dl>

Tidying methods for Spark ML Logistic Regression

ml_logistic_regression_tidiers: Tidying methods for Spark ML Logistic Regression

Description

Usage

Arguments