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)R6Class object.
An object of type Lrnr_base as defined in this package.
An object of type Variable_Tyoe for use in
formatting the outcome
Learner object with methods for training and prediction. See
Lrnr_base for documentation on learners.
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 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.
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_glmnet,
Lrnr_glm,
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