define_h2o_X | R Documentation |
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)
task |
An object of type |
outcome_type |
An object of type |
R6Class
object.
Learner object with methods for training and prediction. See
Lrnr_base
for documentation on learners.
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
.
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
Other Learners:
Custom_chain
,
Lrnr_HarmonicReg
,
Lrnr_arima
,
Lrnr_bartMachine
,
Lrnr_base
,
Lrnr_bayesglm
,
Lrnr_caret
,
Lrnr_cv_selector
,
Lrnr_cv
,
Lrnr_dbarts
,
Lrnr_define_interactions
,
Lrnr_density_discretize
,
Lrnr_density_hse
,
Lrnr_density_semiparametric
,
Lrnr_earth
,
Lrnr_expSmooth
,
Lrnr_gam
,
Lrnr_ga
,
Lrnr_gbm
,
Lrnr_glm_fast
,
Lrnr_glm_semiparametric
,
Lrnr_glmnet
,
Lrnr_glmtree
,
Lrnr_glm
,
Lrnr_grfcate
,
Lrnr_grf
,
Lrnr_gru_keras
,
Lrnr_gts
,
Lrnr_h2o_grid
,
Lrnr_hal9001
,
Lrnr_haldensify
,
Lrnr_hts
,
Lrnr_independent_binomial
,
Lrnr_lightgbm
,
Lrnr_lstm_keras
,
Lrnr_mean
,
Lrnr_multiple_ts
,
Lrnr_multivariate
,
Lrnr_nnet
,
Lrnr_nnls
,
Lrnr_optim
,
Lrnr_pca
,
Lrnr_pkg_SuperLearner
,
Lrnr_polspline
,
Lrnr_pooled_hazards
,
Lrnr_randomForest
,
Lrnr_ranger
,
Lrnr_revere_task
,
Lrnr_rpart
,
Lrnr_rugarch
,
Lrnr_screener_augment
,
Lrnr_screener_coefs
,
Lrnr_screener_correlation
,
Lrnr_screener_importance
,
Lrnr_sl
,
Lrnr_solnp_density
,
Lrnr_solnp
,
Lrnr_stratified
,
Lrnr_subset_covariates
,
Lrnr_svm
,
Lrnr_tsDyn
,
Lrnr_ts_weights
,
Lrnr_xgboost
,
Pipeline
,
Stack
,
undocumented_learner
library(h2o)
suppressWarnings(h2o.init())
# load example data
data(cpp_imputed)
# create sl3 task
task <- sl3_Task$new(
cpp_imputed,
covariates = c("apgar1", "apgar5", "parity", "gagebrth", "mage", "meducyrs"),
outcome = "haz"
)
# train h2o glm learner and make predictions
lrnr_h2o <- Lrnr_h2o_glm$new()
lrnr_h2o_fit <- lrnr_h2o$train(task)
lrnr_h2o_pred <- lrnr_h2o_fit$predict()
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