Lrnr_pooled_hazards: Classification from Pooled Hazards

In jeremyrcoyle/sl3: Pipelines for Machine Learning and Super Learning

Classification from Pooled Hazards

Description

This learner provides converts a binomial learner into a multinomial learner using a pooled hazards model.

Format

R6Class object.

Value

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

Parameters

binomial_learner

The learner to wrap.

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

See Also

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_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

Examples

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()

jeremyrcoyle/sl3 documentation built on Nov. 18, 2024, 4:21 p.m.