h2o.rule_importance

This function returns the table with estimated coefficients and language representations (in case it is a rule) 
for each of the significant baselearners.

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 

h2o.rule_importance function

<dl><dt>model</dt>
<dd>of the interest</dd></dl>

Arguments

This function returns the table with estimated coefficients and language representations (in case it is a rule) 
for each of the significant baselearners. — h2o.rule_importance

<dl>

<dt>model</dt>
<dd>of the interest</dd>

</dl>

This function returns the table with estimated coefficients and language representations (in case it is a rule) 
for each of the significant baselearners.

h2o.rule_importance: This function returns the table with estimated coefficients and language representations (in case it is a rule) for each of the significant baselearners.

Description

Usage

Arguments