Lrnr_h2o_grid - This learner provides facilities for fitting various
types of models with support for grid search over the hyperparameter space of
such models, using an interface to the H2O platform. For details on the
procedures available and any limitations, consult the documentation of the
h2o package.
R6Class object.
Learner object with methods for training and prediction. See
Lrnr_base for documentation on learners.
algorithmAn h2o ML algorithm. For a list, please see http://docs.h2o.ai/h2o/latest-stable/h2o-docs/data-science.html#.
seed=1RNG see to use when fitting.
distribution=NULLSpecifies the loss function for GBM, Deep Learning, and XGBoost.
intercept=TRUEIf TRUE, and intercept term is
included.
standardize=TRUEStandardize covariates to have mean = 0 and SD = 1.
lambda=0Lasso Parameter.
max_iterations=100Maximum number of iterations.
ignore_const_columns=FALSEIf TRUE, drop constant
covariate columns
missing_values_handling="Skip"How to handle missing values.
...Other arguments passed to the h2o algorithm of choice. See http://docs.h2o.ai/h2o/latest-stable/h2o-docs/parameters.html for a list.
Individual learners have their own sets of parameters. Below is a list of shared parameters, implemented by Lrnr_base, and shared
by all learners.
covariatesA character vector of covariates. The learner will use this to subset the covariates for any specified task
outcome_typeA 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_bilstm,
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_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,
define_h2o_X(),
undocumented_learner
library(h2o)
suppressWarnings(h2o.init())
#> Connection successful!
#>
#> R is connected to the H2O cluster:
#> H2O cluster uptime: 7 seconds 520 milliseconds
#> H2O cluster timezone: America/Los_Angeles
#> H2O data parsing timezone: UTC
#> H2O cluster version: 3.36.1.2
#> H2O cluster version age: 1 year, 2 months and 21 days !!!
#> H2O cluster name: H2O_started_from_R_Rachael_jep458
#> H2O cluster total nodes: 1
#> H2O cluster total memory: 2.00 GB
#> H2O cluster total cores: 4
#> H2O cluster allowed cores: 4
#> H2O cluster healthy: TRUE
#> H2O Connection ip: localhost
#> H2O Connection port: 54321
#> H2O Connection proxy: NA
#> H2O Internal Security: FALSE
#> R Version: R version 4.2.0 (2022-04-22)
#>
set.seed(1)
# load example data
data(cpp_imputed)
covars <- c(
"apgar1", "apgar5", "parity", "gagebrth", "mage", "meducyrs",
"sexn"
)
outcome <- "haz"
cpp_imputed <- cpp_imputed[1:150, ]
# create sl3 task
task <- sl3_Task$new(cpp_imputed, covariates = covars, outcome = outcome)
# h2o grid search hyperparameter alpha
h2o_glm_grid <- Lrnr_h2o_grid$new(
algorithm = "glm",
hyper_params = list(alpha = c(0, 0.5))
)
h2o_glm_grid_fit <- h2o_glm_grid$train(task)
pred <- h2o_glm_grid_fit$predict()