feature_frequencies.H2OModel

h2o.feature_frequencies

a fitted <a href="/link/?package=h2o&version=3.30.0.1" h2omodel'="" data-mini-rdoc="h2o::"></a>'>H2OModel object for which prediction is
desired

object

An H2OFrame object in which to look for
variables with which to predict.

newdata

Retrieve the number of occurrences of each feature for given observations 
Available for GBM, Random Forest and Isolation Forest models.

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, Gradient Boosting Machines (including XGBoost), Random Forests,
Deep Neural Networks (Deep Learning), Stacked Ensembles, Naive Bayes, Cox
Proportional Hazards, K-Means, PCA, Word2Vec, as well as a fully automatic
machine learning algorithm (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

H2O.ai 

feature_frequencies.H2OModel function

a fitted <a href='<a href='H2OModel'></a>'>H2OModel</a> object for which prediction is
desired

Retrieve the number of occurrences of each feature for given observations 
Available for GBM, Random Forest and Isolation Forest models. — feature_frequencies.H2OModel

Retrieve the number of occurrences of each feature for given observations 
Available for GBM, Random Forest and Isolation Forest models.

feature_frequencies.H2OModel: Retrieve the number of occurrences of each feature for given observations Available for GBM, Random Forest and Isolation Forest models.

Description

Usage

Arguments

Value

See Also