This learner provides facilities for conditional density estimation using
the condensier
package. Fitting is done with the
fit_density
function.
Lrnr_condensier
R6Class
object.
Learner object with methods for training and prediction. See
Lrnr_base
for documentation on learners.
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.
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
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