H2OClusteringModel-class

This virtual class represents a clustering model built by H2O.

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).

Tomas Fryda

R Interface for the 'H2O' Scalable Machine Learning Platform

Erin LeDell

Navdeep Gill

Spencer Aiello

Anqi Fu

Arno Candel

Cliff Click

Tom Kraljevic

Tomas Nykodym

Patrick Aboyoun

Michal Kurka

Michal Malohlava

Sebastien Poirier

Wendy Wong

Ludi Rehak

Eric Eckstrand

Brandon Hill

Sebastian Vidrio

Surekha Jadhawani

Amy Wang

Raymond Peck

Jan Gorecki

Matt Dowle

Yuan Tang

Lauren DiPerna

Veronika Maurerova

Yuliia Syzon

Adam Valenta

Marek Novotny

H2O.ai 

H2OClusteringModel-class function

<dl>
<dt><code>model_id</code></dt>
<dd>A <code>character</code> string specifying the key for the model fit in the H2O cluster's key-value store.</dd></dl>
<dt><code>algorithm</code></dt>
<dd>A <code>character</code> string specifying the algorithm that was used to fit the model.</dd>
<dt><code>parameters</code></dt>
<dd>A <code>list</code> containing the parameter settings that were used to fit the model that differ from the defaults.</dd>
<dt><code>allparameters</code></dt>
<dd>A <code>list</code> containing all parameters used to fit the model.</dd>
<dt><code>model</code></dt>
<dd>A <code>list</code> containing the characteristics of the model returned by the algorithm.<dl>
 <dt>size </dt>
<dd>The number of points in each cluster.</dd></dl></dd> <dt>totss </dt>
<dd>Total sum of squared error to grand mean.</dd> <dt>withinss </dt>
<dd>A vector of within-cluster sum of squared error.</dd> <dt>tot_withinss </dt>
<dd>Total within-cluster sum of squared error.</dd> <dt>betweenss </dt>
<dd>Between-cluster sum of squared error.</dd>

Slots

The H2OClusteringModel object. — H2OClusteringModel-class

<dl>
<dt><code>model_id</code></dt>
<dd>A <code>character</code> string specifying the key for the model fit in the H2O cluster's key-value store.</dd>


<dt><code>algorithm</code></dt>
<dd>A <code>character</code> string specifying the algorithm that was used to fit the model.</dd>


<dt><code>parameters</code></dt>
<dd>A <code>list</code> containing the parameter settings that were used to fit the model that differ from the defaults.</dd>


<dt><code>allparameters</code></dt>
<dd>A <code>list</code> containing all parameters used to fit the model.</dd>


<dt><code>model</code></dt>
<dd>A <code>list</code> containing the characteristics of the model returned by the algorithm.<dl>
 <dt>size </dt>
<dd>The number of points in each cluster.</dd>

 <dt>totss </dt>
<dd>Total sum of squared error to grand mean.</dd>

 <dt>withinss </dt>
<dd>A vector of within-cluster sum of squared error.</dd>

 <dt>tot_withinss </dt>
<dd>Total within-cluster sum of squared error.</dd>

 <dt>betweenss </dt>
<dd>Between-cluster sum of squared error.</dd>


</dl></dd>


</dl>

The H2OClusteringModel object.

H2OClusteringModel-class: The H2OClusteringModel object.

Description

Arguments

Slots

Details