ML.H2O.glm: ML.H2O.glm
In frbl/OnlineSuperLearner: Online SuperLearner package
Description
Usage
Format
Methods
Description
Wrapper for an H2O glm estimator. From their discription:
Generalized Linear Models (GLM) estimate regression models for outcomes
following exponential distributions. In addition to the Gaussian (i.e.
normal) distribution, these include Poisson, binomial, and gamma
distributions. Each serves a different purpose, and depending on
distribution and link function choice, can be used either for prediction or
classification.
Usage
1
Format
An object of class R6ClassGenerator of length 24.
Methods
initialize(nfolds = 1, alpha = 0.5, family = "gaussian") -
Creates a new h2o glm model
@param nfold integer (default = 1) specify the number of folds for
cross-validation.
@param alpha double (default = 0.5) the alpha parameter controls the distribution
between the l1 (LASSO) and l2 (ridge regression) penalties. A value of
1.0 for alpha represents LASSO, and an alpha value of 0.0 produces
ridge reguression.
@param family string (default = "binomial") the family to use for the
estimator. Generally this should be binomial (since we are running
various logistic regressions). However, if you have a good reason to
use a different specification, change it here.
frbl/OnlineSuperLearner documentation built on Feb. 9, 2020, 9:28 p.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Description Usage Format Methods
Description
Wrapper for an H2O glm estimator. From their discription: Generalized Linear Models (GLM) estimate regression models for outcomes following exponential distributions. In addition to the Gaussian (i.e. normal) distribution, these include Poisson, binomial, and gamma distributions. Each serves a different purpose, and depending on distribution and link function choice, can be used either for prediction or classification.
Usage
1 |
Format
An object of class R6ClassGenerator of length 24.
Methods
initialize(nfolds = 1, alpha = 0.5, family = "gaussian")-
Creates a new h2o glm model
@param nfold integer (default = 1) specify the number of folds for cross-validation. @param alpha double (default = 0.5) the alpha parameter controls the distribution between the l1 (LASSO) and l2 (ridge regression) penalties. A value of 1.0 for alpha represents LASSO, and an alpha value of 0.0 produces ridge reguression.
@param family string (default = "binomial") the family to use for the estimator. Generally this should be binomial (since we are running various logistic regressions). However, if you have a good reason to use a different specification, change it here.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Embedding an R snippet on your website
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.