This meta-learner provides fitting procedures for any pairing of loss
function and metalearner function, subject to constraints. The optimization
problem is solved by making use of solnp
, using
Lagrange multipliers. For further details, consult the documentation of the
Rsolnp
package.
Lrnr_solnp
R6Class
object.
Learner object with methods for training and prediction. See
Lrnr_base
for documentation on learners.
learner_function=metalearner_linear
A function(alpha, X) that takes a vector of covariates and a matrix of data and combines them into a vector of predictions. See metalearners for options.
loss_function=loss_squared_error
A function(pred, truth) that takes prediction and truth vectors and returns a loss vector. See loss_functions for options.
make_sparse=TRUE
If TRUE, zeros out small alpha values.
convex_combination=TRUE
If TRUE
, constrain alpha to
sum to 1.
init_0=FALSE
If TRUE, alpha is initialized to all 0's, useful for TMLE. Otherwise, it is initialized to equal weights summing to 1, useful for SuperLearner.
...
Not currently used.
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_condensier
,
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_subset_covariates
,
Lrnr_svm
, Lrnr_tsDyn
,
Lrnr_xgboost
, Pipeline
,
Stack
, define_h2o_X
,
undocumented_learner