This learner provides facilities for conditional density estimation using the condensier package. Fitting is done with the fit_density function.

Lrnr_condensier

Format

R6Class object.

Value

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

Parameters

bin_method=c("equal.mass", "equal.len", "dhist")

The type of smoothing to be performed. See documentation of the condensier package for details.

nbins=5

The number of observations per bin. See documentation of the condensier package for details.

max_n_cat = 20

Maximum number of unique levels for categorical outcomes. See documentation of the condensier package for details.

pool = FALSE

Whether pooling of data across bins should be performed. See documentation of the condensier package for details.

max_n_bin=NA

Maximum number of observations per single bin for continuous outcome. See documentation of the condensier package for details.

parfit=FALSE

Whether to invoke parallelization in the fitting procedure. See documentation of the condensier package for details.

bin_estimator=make_learner(Lrnr_glm_fast, family=binomial())

The classification algorithm to be used in the fitting process. See documentation of the condensier package for details.

intrvls=NULL

An interval range to be used for custom bin definitions. See documentation of the condensier package for details.

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_bilstm, Lrnr_cv, Lrnr_define_interactions, Lrnr_expSmooth, Lrnr_glm_fast, Lrnr_glmnet, Lrnr_glm, Lrnr_h2o_grid, Lrnr_hal9001, Lrnr_independent_binomial, Lrnr_lstm, Lrnr_mean, Lrnr_nnls, Lrnr_optim, Lrnr_pca, Lrnr_pkg_SuperLearner, Lrnr_randomForest, Lrnr_ranger, Lrnr_rpart, Lrnr_rugarch, Lrnr_sl, Lrnr_solnp_density, Lrnr_solnp, Lrnr_subset_covariates, Lrnr_svm, Lrnr_tsDyn, Lrnr_xgboost, Pipeline, Stack, define_h2o_X, undocumented_learner