h2o.coef_norm

an <a href="/link/?package=h2o&version=3.20.0.8" h2omodel'="" data-mini-rdoc="h2o::"></a>'>H2OModel object.

object

Return coefficients fitted on the standardized data (requires standardize = True, which is on by default). These coefficients can be used to evaluate variable importance.

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 'H2O'

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 

h2o.coef_norm function

an <a href='<a href='H2OModel'></a>'>H2OModel</a> object.

Return coefficients fitted on the standardized data (requires standardize = True, which is on by default). These coefficients can be used to evaluate variable importance. — h2o.coef_norm

Return coefficients fitted on the standardized data (requires standardize = True, which is on by default). These coefficients can be used to evaluate variable importance.

h2o.coef_norm: Return coefficients fitted on the standardized data (requires standardize = True, which is on by default). These coefficients can be used to evaluate variable importance.

Description

Usage

Arguments