This learner provides converts a binomial learner into a multinomial learner using a pooled hazards model.
R6Class object.
Learner object with methods for training and prediction. See
Lrnr_base for documentation on learners.
binomial_learnerThe learner to wrap.
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_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_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(data.table)
set.seed(74294)
n <- 500
x <- rnorm(n)
epsilon <- rnorm(n)
y <- 3 * x + epsilon
data <- data.table(x = x, y = y)
task <- sl3_Task$new(data, covariates = c("x"), outcome = "y")
# instantiate learners
hal <- Lrnr_hal9001$new(
lambda = exp(seq(-1, -13, length = 100)),
max_degree = 6,
smoothness_orders = 0
)
hazard_learner <- Lrnr_pooled_hazards$new(hal)
density_learner <- Lrnr_density_discretize$new(
hazard_learner,
type = "equal_range",
n_bins = 5
)
# fit discrete density model to pooled hazards data
set.seed(74294)
fit_density <- density_learner$train(task)
pred_density <- fit_density$predict()