Description Usage Format Value Parameters Common Parameters See Also

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.

1 |

`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_dbarts`

,
`Lrnr_define_interactions`

,
`Lrnr_expSmooth`

,
`Lrnr_glm_fast`

, `Lrnr_glmnet`

,
`Lrnr_glm`

, `Lrnr_grf`

,
`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_stratified`

,
`Lrnr_subset_covariates`

,
`Lrnr_svm`

, `Lrnr_tsDyn`

,
`Lrnr_xgboost`

, `Pipeline`

,
`Stack`

, `define_h2o_X`

,
`undocumented_learner`

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