The Highly Adaptive LASSO is an estimation procedure that generates a design
matrix consisting of basis functions corresponding to covariates and
interactions of covariates and fits LASSO regression to this (usually) very
wide matrix, recovering a nonparametric functional form that describes the
target prediction function as a composition of subset functions with finite
variation norm. This implementation uses the hal9001
R package, which
provides both a custom implementation (based on the origami
package)
of the CV-LASSO as well the standard call to cv.glmnet
from the
glmnet
package.
Lrnr_hal9001
R6Class
object.
Learner object with methods for training and prediction. See
Lrnr_base
for documentation on learners.
degrees="degrees"
The highest order of interaction terms for
which the basis functions ought to be generated. The default (NULL
)
corresponds to generating basis functions for the full dimensionality of
the input matrix.
fit_type="fit_type"
The specific routine to be called when
fitting the LASSO regression in a cross-validated manner. Choosing the
glmnet
option will result in a call to cv.glmnet
while
origami
will produce a (faster) call to a custom routine based on
the origami
package.
n_folds="n_folds"
Integer for the number of folds to be used when splitting the data for cross-validation. This defaults to 10 as this is the convention for v-fold cross-validation.
use_min="use_min"
Determines which lambda is selected from
cv.glmnet
. TRUE
corresponds to "lambda.min"
and
FALSE
corresponds to "lambda.1se"
.
...
Other parameters passed directly to
fit_hal
. See its documentation for details.
Other Learners: Custom_chain
,
Lrnr_HarmonicReg
, Lrnr_arima
,
Lrnr_bartMachine
, Lrnr_base
,
Lrnr_bilstm
, Lrnr_condensier
,
Lrnr_cv
,
Lrnr_define_interactions
,
Lrnr_expSmooth
,
Lrnr_glm_fast
, Lrnr_glmnet
,
Lrnr_glm
, Lrnr_h2o_grid
,
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