Definition of h2o type models. This function is for internal use only. This function uploads input data into an h2o.Frame, allowing the data to be subset to the task$X data.table by a smaller set of covariates if spec'ed in params.

This learner provides faster fitting procedures for generalized linear models by using the h2o package and the h2o.glm method. The h2o Platform fits GLMs in a computationally efficient manner. For details on the procedure, consult the documentation of the h2o package.

define_h2o_X(task, outcome_type = NULL)

Format

R6Class object.

Arguments

task

An object of type Lrnr_base as defined in this package.

outcome_type

An object of type Variable_Tyoe for use in formatting the outcome

Value

Learner object with methods for training and prediction. See Lrnr_base for documentation on learners.

Parameters

intercept=TRUE

If TRUE, and intercept term is included.

standardize=TRUE

Standardize covariates to have mean = 0 and SD = 1.

lambda=0

Lasso Parameter.

max_iterations=100

Maximum number of iterations.

ignore_const_columns=FALSE

If TRUE, drop constant covariate columns

missing_values_handling="Skip"

How to handle missing values.

...

Other arguments passed to h2o.glm.

Common Parameters

Individual learners have their own sets of parameters. Below is a list of shared parameters, implemented by Lrnr_base, and shared by all learners.

covariates

A character vector of covariates. The learner will use this to subset the covariates for any specified task

outcome_type

A variable_type object used to control the outcome_type used by the learner. Overrides the task outcome_type if specified

...

All other parameters should be handled by the invidual learner classes. See the documentation for the learner class you're instantiating