The Highly Adaptive Lasso (HAL) is a nonparametric regression function that
has been demonstrated to optimally estimate functions with bounded (finite)
variation norm. The algorithm proceeds by first building an adaptive basis
(i.e., the HAL basis) based on indicator basis functions (or higher-order
spline basis functions) representing covariates and interactions of the
covariates up to a pre-specified degree. The fitting procedures included in
this learner use fit_hal
from the hal9001
package. For details on HAL regression, consider consulting the following
Benkeser and van der Laan (2016)
),
Coyle et al. (2020)
),
Hejazi et al. (2020)
).
A learner object inheriting from Lrnr_base
with
methods for training and prediction. For a full list of learner
functionality, see the complete documentation of Lrnr_base
.
...
: Arguments passed to fit_hal
. See
it's documentation for details.
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_glmnet
,
Lrnr_glm
,
Lrnr_grf
,
Lrnr_gru_keras
,
Lrnr_gts
,
Lrnr_h2o_grid
,
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_pooled_hazards
,
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
data(cpp_imputed)
covs <- c("apgar1", "apgar5", "parity", "gagebrth", "mage", "meducyrs")
task <- sl3_Task$new(cpp_imputed, covariates = covs, outcome = "haz")
# instantiate with max 2-way interactions, 0-order splines, and binning
# (i.e., num_knots) that decreases with increasing interaction degree
hal_lrnr <- Lrnr_hal9001$new(
max_degree = 2, num_knots = c(20, 10), smoothness_orders = 0
)
hal_fit <- hal_lrnr$train(task)
hal_preds <- hal_fit$predict()