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Lrnr_haldensify.Rd
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Lrnr_haldensify.Rd
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% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/Lrnr_haldensify.R
\docType{class}
\name{Lrnr_haldensify}
\alias{Lrnr_haldensify}
\title{Conditional Density Estimation with the Highly Adaptive LASSO}
\format{
\code{\link{R6Class}} object.
}
\value{
Learner object with methods for training and prediction. See
\code{\link{Lrnr_base}} for documentation on learners.
}
\description{
Conditional Density Estimation with the Highly Adaptive LASSO
}
\section{Parameters}{
\describe{
\item{\code{grid_type = c("equal_range", "equal_mass")}}{\code{character}
indicating the strategy to be used in creating bins along the observed
support of the outcome variable. For bins of equal range, use
"equal_range" (based on \code{\link[ggplot2]{cut_interval}}). To ensure
each bin has the same number of observations, use "equal_mass" (based on
\code{\link[ggplot2]{cut_number}}).
}
\item{\code{n_bins = c(3, 5)}}{Only used if \code{type} is set to
\code{"equal_range"} or \code{"equal_mass"}. This \code{numeric} value
indicates the number of bins that the support of the outcome variable is
to be divided into.
}
\item{\code{lambda_seq = exp(seq(-1, -13, length = 1000L))}}{\code{numeric}
sequence of values of the regulariztion parameter of the Lasso regression,
to be passed to to \code{\link[hal9001]{fit_hal}}.
}
\item{\code{...}}{ Other parameters passed directly to
\code{\link[haldensify]{haldensify}}. See its documentation for details.
}
}
}
\seealso{
Other Learners:
\code{\link{Custom_chain}},
\code{\link{Lrnr_HarmonicReg}},
\code{\link{Lrnr_arima}},
\code{\link{Lrnr_bartMachine}},
\code{\link{Lrnr_base}},
\code{\link{Lrnr_bayesglm}},
\code{\link{Lrnr_bilstm}},
\code{\link{Lrnr_bound}},
\code{\link{Lrnr_caret}},
\code{\link{Lrnr_cv_selector}},
\code{\link{Lrnr_cv}},
\code{\link{Lrnr_dbarts}},
\code{\link{Lrnr_define_interactions}},
\code{\link{Lrnr_density_discretize}},
\code{\link{Lrnr_density_hse}},
\code{\link{Lrnr_density_semiparametric}},
\code{\link{Lrnr_earth}},
\code{\link{Lrnr_expSmooth}},
\code{\link{Lrnr_gam}},
\code{\link{Lrnr_gbm}},
\code{\link{Lrnr_glm_fast}},
\code{\link{Lrnr_glmnet}},
\code{\link{Lrnr_glm}},
\code{\link{Lrnr_grf}},
\code{\link{Lrnr_gru_keras}},
\code{\link{Lrnr_gts}},
\code{\link{Lrnr_h2o_grid}},
\code{\link{Lrnr_hal9001}},
\code{\link{Lrnr_hts}},
\code{\link{Lrnr_independent_binomial}},
\code{\link{Lrnr_lightgbm}},
\code{\link{Lrnr_lstm_keras}},
\code{\link{Lrnr_lstm}},
\code{\link{Lrnr_mean}},
\code{\link{Lrnr_multiple_ts}},
\code{\link{Lrnr_multivariate}},
\code{\link{Lrnr_nnet}},
\code{\link{Lrnr_nnls}},
\code{\link{Lrnr_optim}},
\code{\link{Lrnr_pca}},
\code{\link{Lrnr_pkg_SuperLearner}},
\code{\link{Lrnr_polspline}},
\code{\link{Lrnr_pooled_hazards}},
\code{\link{Lrnr_randomForest}},
\code{\link{Lrnr_ranger}},
\code{\link{Lrnr_revere_task}},
\code{\link{Lrnr_rpart}},
\code{\link{Lrnr_rugarch}},
\code{\link{Lrnr_screener_augment}},
\code{\link{Lrnr_screener_coefs}},
\code{\link{Lrnr_screener_correlation}},
\code{\link{Lrnr_screener_importance}},
\code{\link{Lrnr_sl}},
\code{\link{Lrnr_solnp_density}},
\code{\link{Lrnr_solnp}},
\code{\link{Lrnr_stratified}},
\code{\link{Lrnr_subset_covariates}},
\code{\link{Lrnr_svm}},
\code{\link{Lrnr_tsDyn}},
\code{\link{Lrnr_ts_weights}},
\code{\link{Lrnr_xgboost}},
\code{\link{Pipeline}},
\code{\link{Stack}},
\code{\link{define_h2o_X}()},
\code{\link{undocumented_learner}}
}
\concept{Learners}
\keyword{data}